Time_Series_Forecasting_Traditional_Methods.ipynb

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   "source": [
    "<h1 align='center'> Time Series Forecasting General Methods </h1>"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "We are done with analyze the data at the hand and getting graphical and numerical insights from the data. We can use the statistics of the existing data to forecast in future. \n",
    "\n",
    "## **Contents**\n",
    "\n",
    "- **Forecasting with Holt-Winters method**\n",
    "- **Time Series Stationarity and Differencing**\n",
    "- **Descriptive statistics and tests**\n",
    "- **ARMA**\n",
    "- **ARIMA**\n",
    "- **SARIMA**\n",
    "- **SARIMAX**\n",
    "- **VAR**\n",
    "- **VARMA**\n"
   ]
  },
  {
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   "source": [
    "## Forecasting with Holt-Winter method\n",
    "\n",
    "This method is another name of Triple Exponential Smoothing. It takes seasonality into account. When we expect regular fluctuations in the future, this model attempts to map the seasonal behavior.\n",
    "\n",
    "I am using same Sales Data of Furnitures. The main objective here is to divide the dataset into 2 parts namely train and test section and then evaluate the performance of the model.\n"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the sales data of furniture\n",
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df = df[df['Category']=='Furniture']\n",
    "df = df.groupby(by='Order Date').agg({'Sales':sum})\n",
    "df.sort_index(inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the resample frequency and make it monthly sales instead of daily.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>6242.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>1839.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>14573.956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>7944.837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Order Date           \n",
       "2014-01-01   6242.525\n",
       "2014-02-01   1839.658\n",
       "2014-03-01  14573.956\n",
       "2014-04-01   7944.837"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resample the data on Monthly level\n",
    "df = df.resample('MS').sum()\n",
    "\n",
    "df.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Test split\n",
    "\n",
    "Divide the data into `80:20` to train to test ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total df size 48\n",
      "Total train data size 38\n",
      "Total test data size 10\n"
     ]
    }
   ],
   "source": [
    "#total length\n",
    "n = len(df)\n",
    "#train size\n",
    "m = int(n*0.8)\n",
    "\n",
    "train_data = df.iloc[0:m]\n",
    "test_data = df.iloc[m:n]\n",
    "\n",
    "print(f\"Total df size {len(df)}\")\n",
    "print(f\"Total train data size {len(train_data)}\")\n",
    "print(f\"Total test data size {len(test_data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10-01</th>\n",
       "      <td>11872.5770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>31783.6288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>36678.7150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>5964.0320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>6866.3374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales\n",
       "Order Date            \n",
       "2016-10-01  11872.5770\n",
       "2016-11-01  31783.6288\n",
       "2016-12-01  36678.7150\n",
       "2017-01-01   5964.0320\n",
       "2017-02-01   6866.3374"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/holtwinters/model.py:915: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
    "\n",
    "fitted_model = ExponentialSmoothing(train_data['Sales'], trend='mul', seasonal='mul', seasonal_periods=12).fit()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating against test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = fitted_model.forecast(len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-03-01    13763.527009\n",
       "2017-04-01    12828.424876\n",
       "2017-05-01    13100.634157\n",
       "2017-06-01    10880.026770\n",
       "2017-07-01    14485.001836\n",
       "2017-08-01    10032.360087\n",
       "2017-09-01    29777.813730\n",
       "2017-10-01    14468.440315\n",
       "2017-11-01    30588.479516\n",
       "2017-12-01    31461.293604\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot test and train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Sales'].plot(legend=True, label='TRAIN DATA')\n",
    "test_data['Sales'].plot(legend=True, label='TEST DATA', figsize=(12, 6)).autoscale(axis='x', tight=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Actual vs Predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Sales'].plot(legend=True, label='TRAIN DATA')\n",
    "test_data['Sales'].plot(legend=True, label='TEST DATA', figsize=(12, 6)).autoscale(axis='x', tight=True)\n",
    "test_predictions.plot(legend=True, label='PREDICTION')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evalution Metrics\n",
    "\n",
    "Since this is a type of regression problem we can use regression metrics like *Mean Squared Error(MSE)* or *Root Mean Squared Error(RMSE)* or *Mean Absolute Error(MAE)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error of the above model is 4138.682460955162\n"
     ]
    }
   ],
   "source": [
    "mae_error = mean_absolute_error(test_data, test_predictions)\n",
    "\n",
    "print(f\"Mean absolute error of the above model is {mae_error}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error of the above model is 23713961.102707732\n"
     ]
    }
   ],
   "source": [
    "mse_error = mean_squared_error(test_data, test_predictions)\n",
    "\n",
    "print(f\"Mean squared error of the above model is {mse_error}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean squared error of the above model is 4869.698255817062\n"
     ]
    }
   ],
   "source": [
    "rmse_error = np.sqrt(mean_squared_error(test_data, test_predictions))\n",
    "\n",
    "print(f\"Root mean squared error of the above model is {rmse_error}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20255.689980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9463.329001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9065.958100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12720.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17983.072450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27242.171550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>37056.715000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales\n",
       "count     10.000000\n",
       "mean   20255.689980\n",
       "std     9463.329001\n",
       "min     9065.958100\n",
       "25%    12720.235000\n",
       "50%    17983.072450\n",
       "75%    27242.171550\n",
       "max    37056.715000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecast in future\n",
    "\n",
    "We evaluated the model by training on train data and testing it on the unknown points to the model. In this section we will see how model's forecast looks on future points which is not even in our test set.\n",
    "\n",
    "We will train the model on whole dataset and then will try to predict on furure points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/holtwinters/model.py:915: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "fitted_model = ExponentialSmoothing(df['Sales'], trend='mul', seasonal='mul', seasonal_periods=12).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "future_preds = fitted_model.forecast(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01    10145.196758\n",
       "2018-02-01     5276.646045\n",
       "2018-03-01    14046.143518\n",
       "2018-04-01    12638.938600\n",
       "2018-05-01    15347.806822\n",
       "2018-06-01    14398.498944\n",
       "2018-07-01    15575.177149\n",
       "2018-08-01    12050.471323\n",
       "2018-09-01    32198.645769\n",
       "2018-10-01    15228.732215\n",
       "2018-11-01    34823.955051\n",
       "2018-12-01    37118.533234\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sales'].plot(figsize=(12, 8), legend=True, label='Exisiting Points')\n",
    "future_preds.plot(figsize=(12, 8), legend=True, label='Future Points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Stationarity and Differencing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stationarity\n",
    "\n",
    "Time series said to be stationary if it does not exhibit trends or seasonality i.e fluctuations in the data are entirely due to outside factors and noises\n",
    "\n",
    "Example:\n",
    "\n",
    "`Samples.csv` file contains some dummy data and we can use it to illustrate satationarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>36</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>58</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>61</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a   b  c   d\n",
       "1950-01-01  36  27  0  67\n",
       "1950-02-01  58  22  3  31\n",
       "1950-03-01  61  17  5  67\n",
       "1950-04-01  37  15  8  47\n",
       "1950-05-01  66  13  8  62"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/samples.csv', index_col=0, parse_dates=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['a'].plot(ylim=[0, 100], title='STATIONARY DATA').autoscale(axis='x', tight=True)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check MA of attribute `a`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Moving Average'}>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['a_ma'] = df['a'].rolling(12).mean()\n",
    "\n",
    "df['a_ma'].plot(ylim=[0, 100], title='Moving Average')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean seems have a nearly constant value."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Non stationary Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['b'].plot(ylim=[0,100],title=\"NON-STATIONARY DATA\").autoscale(axis='x',tight=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate MA with window size of 12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Moving Average'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['b_ma'] = df['b'].rolling(12).mean()\n",
    "df['b_ma'].plot(ylim=[0, 100], title='Moving Average')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean does not seem same between two equidistance timestamp."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Differencing\n",
    "\n",
    "Non-stationary data can be made to look stationary through <em>differencing</em>. A simple differencing method calculates the difference between consecutive points.\n",
    "\n",
    "<div class=\"alert alert-info\"><h3>Related Functions:</h3>\n",
    "<tt><strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.tools.diff.html'>statespace.tools.diff</a></strong><font color=green>(series[, k_diff, …])</font>&nbsp;&nbsp;Difference a series simply and/or seasonally along the zero-th axis.</tt></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+ezXU/PlCOjGUjtdhZEscsx3ESuuuG3z4jUDVSj7rFeW6GWrlWFMkt16ToVQDMSz1axpmC4kYAej9eNYCRrPVu4O7Fe3qs9ckS23kQ8Bh8r0b+USMkE3GhybjdaVqIGWVNBhGTb7UhckPYxZxJ9z8F9/C39/1dM/fK4U08up56resATAcCVFCCDSaLYxlZLWVYSQhH/jaQfzk397X03dU3TyKe1Y9L9rxGhEGMfKpRAypRGxoatcsVw1sHc8AGFYj3zmxZyM5XquNJk4sVXF0obckOqPZQt0Mxxhdck3PBcqczxeHoLQBG8PxbBLAcGZlH1+s4Fiv1zPie1Yz+VVAsd6nXNN0jPywsE7VyA8jU+IJNVCT30BMfqEi27P1ypJ7IRWqIew9Tt5hmMPA5HnSGc9IIz8sEqeKaqPZdz4CEJGRV5m8NvLRoG/Hq9lCMh5DOjFccfJbJ4aYyVvacNCDpF6DYTmnQVi0enBWe6zdzz6gMPILT4aZZKwvuWbckkbWIyHqiw+fwLNKf1m+nuNZOaZhbAFYM1o9RzFFnfle63FloI18CPADYDRFT060htlCKm7JNUNikFZqBjaPpQEM53K41IXJq7LXsEfXcKPsnpm8FfUURn7hyXAym+q9rEGzhclcCoCzWl0rLFcN/MatD+OfHzzuGg/gMPlhbAFYM2VSZK92gBHFPesKPlgrJk9EHyWiOSI6oLw2RUR3ENGT1u9NUexrPaA+AL2wx3qzhXQihnQiPhSss242UTNamMwlkUnGhiY2WgVPqEFsaVj75vph0ZJreg1VLSvyYLckJWa7k7lkXzLCpnzKtc+1wpzF4NUx8yTFRn4YSQjfl72ca5bF0hHJtr36paJi8v8A4BWe134XwDeEEPsAfMP6f0Oi3KeRN1TH6xCwEg6fnMgmkUslhvIhKtXDyTWFIUowC0K/TL6ohDN2Y+c8UW/KpfpIhhIYzyQQo3Uw8sU6AE+kSNMt1wyjz4hZdG9G3gmljuKe9TtnnRCJkRdCfBvAgufl1wH4mPX3xwC8Pop9rQfUh7QXY82O13QiBqMp0OqztO+t9x/F737ukb6+q2KlKh/k8WwS2WQ8ck3ebLbwlv9zL+4+fKbvbXQ18obywAy5XMOafO9MPnxHMdvI55O9lzVoSjkxn064Jpa1wFyRmXy7I91m8hEb+Y/dfQTv/uKB7h/sAL4vvWP7lU8+iNu/d8r3O3xc+YiM/HoxeT9sEULwUZ8GsMXvQ0T0diLaT0T75+f9U9nXG70wKxWqJg/0Hw1y58F5fOXA6b6+q0Jl8tlUPHKmtFgxcPfhs/jG43N9b6NrdM0QZhEHYcGWa/qLrgG6kwpOhprI9sPkZWBAIZ1YB7lGMnn1+LiWDodQRh12fOfBOXz10cGeI/aTqOdaCIGvHDiNbz7hf99zCGXURp5oiGrXCCks+o5GCHGLEOJ6IcT1s7OzazGcnuF+6Ho08okYUvFYz99VsVBpYKVmDNzkg0saOEw+2geb5Z/D86W+t2E7XgMMIz/4Y5mNwOTl+e7VyKs+oG6G25FrkqiZzZ4KjZlNgWRCMvnyGpe4cOSa9josdnRNxCRkpWZisWwMVIyt5iPX1M0WhABOLdd8v8OGOJ+KJimS9x12NbuaRv5ZItoGANbv/uldH1gsN2znzqBQH4C+5JpkXP7fp5FfLDcgBHquZ++Fl8lH/RDx9qIw8kGRJaomv2Giaxq9VXnshclXjaYtuQgRvFo8sVRtq0/TaLaQjBMK6yLXtGvyXGZhteSa5aqBRrOFcp8ypRDCV65hQnJquer7PUORawaRbRlMFsNq/Ktp5G8D8Dbr77cB+OIq7qsNf/SlR/FLn3gwkm0NKtekbSbf383FURpLlcGM/EpNMfLJeOSOV14ZHF+s9i0FlZTIEr+VS91sgQjIp4ZfruHrJkRvFSJ7WTlWG01kktLv0+nzb/jw3fjQvx1yvWY0W0jG1kuusTR5n/BClmuilhOZ5LCvpFc0mi3wLamOrWKwka/5TuYNxfGq/t8v6mYTaSugY80cr0T0aQD3ALiYiI4T0c8DeB+AlxHRkwButv5fM8wV63hqvhzJtsp1E5O53nVClmvSyZj9f69otQQWLePORqNfLFvbGc8kkUtF73hldiME8PSZ/s59Nyd3w2zZN/iwG/kFxZj0Ioe45ZoumrzRRDYVt1eLfp+vNpo4uVzDQrnuet1oCiQThHw6vuYZr/MWk/crOMfXN3K5xjLyC30aeXWiVgkS/11pNO3gBhUOk7dW9IMaeaOFTDKOVDzcM5AYaG8WhBBvDnjrpVFsvx+UG00sVw1UGiZyqcEOs1w3MZVPYali9HSB6hFo8sWaaTPapergck02GUcqEZNMPmq5RrnxD8+XcOm28Z63oUoK1Uaz7drVzRbSiTiS8Vhk/TJXA0IILFYa2DyWxlyx3tOqqScmbzSRTcaRYSbvQ0JOWjKC1yA4jtfkmpcatuUaHyafSsSQibjeU81o2udyoU+ypE5I6rjVa3tqpYoJixAyVLkGGDy/g5m8fAbOgYzXuZUa/uyrT7Qt7SvWTRvkDOkFxbqJaStppJfU8UbTYZ1AfxdXvSGXI5BrJqylcDYVvVyjThqH5/pj8qWaaafa++nydbOp5B4ML5Mv1U0YTYGdm7IAemPyPWnyjSYyyTgyHZj8aesZ8J4vx8jH19TIl+umvT+/7M1kPBbq/hRC4K+/eQgHTxe77lPtoxAk1zRbAu/7yhP2KsMLlcmrJZ7V+/7UUru94aihQlRG3mghnQz/DGx4I3/bd0/iw3cextNn3M4+liL8TnovEELYTB7oXZOXtWv6X6apS8ulQeWaqmFHLuRWwfHK5zwVj/XlfG22BKpGEzMdyi7UTc4ijkXSgGG1wJE1OzflAPSWEFWsmfZkHCa6JpuKd9TkTy4FMXmBZJyQTydQWsMWgMziifxjvlPxGDLJeNeyBkfOVvCBrx3E/33oeMfPAe4+CkFyzeH5Ej7yrcP4l0dO+r6vjkf9W41S8yOVbZr8gEa+ZjaRSUi55pxg8oct3d37EPGJD/J4h0XNkM6Wqbw0PL1cIG+cfD/LT5V1RCHX2EzekmuifLCZRV68dawvI89sd6aQdm1PBRv5ZJyGWq7hFdgOi8n3Eq5abpiYLjCpCKHJd2HybHhUktFqyTpMyXgMhUwCzZZYs5URO123jWfc0TWKXBMmMOCuQzLpbn7Fn3mrWFEi04J8W7yCOhzgy6v5RNR4X/ezN165ZtCoMJXJr3d0zZqAjYl3OVy2w5oGY/JcZ3u6HyavZLzK/3tnnqpcM2h0zXLVVOSaRF99azuBmfwVOybw1Hy551AxrkA528nIGy2kEvGhKt/sB56cbbmmByZfrjcVeTCkJm8b+fbP8zPgKu7WcqQRZphrJdkwk985lfNtgJGKy7DjbgXaOLN6LkBeUeFm8v7PUbc8D5dco/zN9z0RcNJPrvEw+UGfuZrZtPxS4YjOhjfyT7GRVx4io9myZ7hBmTxvNyyzYjQtppRKRMPkZwqpgeWalaphxyBnk9F3h+JtXb59HFWjaTv8woKZ1Ix1rv0Mlh0+Fo/b53hQfPBrB/HL/xhNuC1jwTbyUq7ppdxwsWZiOh880amoNprIuOQaPyYvr0Pd1R9XnjeOkwfWrgUgG+VdUznfsgbJRAzZZMyle3vRagncc/istb3uRI6NfCoRC9Tky12NvL8Oz/f99oksTq+03/Nsi/JRhVAaLWTOFSa/VGngTKm9PohquAZl8mx4WJMPK9eokQIOk+/P8ZpKxLB9MjuwXLNSNewYZI5aiVKXrxkyZnvf5gKA4GVvEEp1t1zjNzY1hBKIpnTrA0cW8L0TywNvR8XCQExelWs6H1/NaHVl8rbj1Yc1J61EKmAtmXwNqXgMW8bTqCtZujzxpOJSrumkyT9+egWLFQOTuWQoJs+hjedP5QKja9iGPLtS9008DGqazn/vns37+gAbSsYrEEV0jRphNuJGXjUi6g3qcoQM6HjlRKheHa91Zek5KJOfyqUwmUsNJNc0WwLFuiPXZKwbrhphOnulYSKbjGMPG/m53nR5vobTXTT5lKXJ8/+DYr5Yjzytf6HSQCJG2GK3Wgy3fXY+h5UHHblG3mN+58x2vDY7yzVrlRA1v1LH7Fga2WQcLeEY97o98RAyXTT5uw9JFv+qK7dhqWJ0XWEzkz9/Oh/M5JWJ2C/Hxs+wAw6p3DNbwMnlapufy7AyiwfJl/GOI91DhNkGN/KOEanU20/6plyyZ8nAC77xJ7JJJGIUWq7xJnYA7uVyWCyUDWzKpzCZTQ4k1zAzsZl8ko18dLp2tdFCLpXAdD6FiWyyq/P1yJkynnzWCX/zyjV+TJ5ZTLqPsFSj2cI3D7ZX15gr1l33jx++eXCup30tlhvYlE/Z5zksk+eJbty638LINdmUwuQ992e5bmLF7jTVLtek+tTk7zl8to3t1owm/v3J7kUG54rSyHPUGY+ZjSERdWXydx0+g92zeVy1c0Jus4vzdblqIJeKY/N4OtDxqk7Efvcun79knNxlDYwmknHCeZb8tOxZcRtWlF0yHo2Rr5tOMtQ5wORLNqNzMfm6M7MWa+ZAy1D+bj6d6Kkhd8Ml1/S/TFusNDCVT2IylxxIrlHr1gAyTh7ovQxuJ1QNE5lkDESEPbP5rkb+Pf/yOH77n75r/8+rJg6h9DNwDbNpRxYAvck1XzlwGj/79w+44qorDXl/NBQ/jhdHzpTxs3//AG77rn9onR8Wyg1M51OIxainYnA80RXSCWSS8Y5MTQjJ+jNJRZP33J8sV3qLY7Fck7BCKIHwRn6p0sBb/vZe/NN+d+jilx85hZ/+u/u7+sHmijVsHkvbqw8eM0ejAUA6GQ8kIA2zhfufXsBNe2aweSxjbbOzkV+xIsumciksVgzfoAAmh4kY4ZDPKpQnHVnx0x1pk0nGsc1qq+l1vnI+wqDVaBnnVDLU4bkyLpzJWyn6zg3KS++9lmxwegA2zzf+WDrRU2cXO+Y3EevoFOuGxXIDm3KSyS9X/W/OMAg08hFq8mqG6p7ZQldNfqVm2FIC4Bi4jtE1dghl76zo2EIFAHB8sWK/pjLAIEPMGq666uiGxYq8boBMZw/r4C4rpCKdiHVk8jwBuDR5zz3GBvf86bxvGzq3XBNujKdXahACOOspk3CmJP9fDIheYcwV69g8nm4rxWA0W0haz0o2GVwK+5HjS6g0mrhxzzRmLUIw38X5yuHDm/IpKV36OJnLDRPpRAznT+d8CQqPZ1MuiaqnxEEu5Rh57yTXaMpQ1VRETL5m9FbaY0Mb+afmS9gzW0AulUDJJdfIC7hnVhp5v7CmsHA/dPHwTN7W5OMDXdyFSgNTeanJy0qU/TFvr5HPWUa+UwRDr6g0pD4MAHs2FzBfrLctXVXUjCbOlhswLYPD0Q2249WHybGR74cV8cN3UnHGqwwwiMly1Ekvsf8L5Ybtx8mlEqGNPNetKWSkke/E5FmzzioFyryOV/ZJXTCTc9dut6NrZJw8AJTq4VaKPDF6ry3/32lFUDebWKoY2DyWsScmHpfK5DPJ4AnurkNnQQS8YM80No/Le6Ubk1+2gg6m8vL+93O+VupN5NOJQILC59bbarFi+UW2TUgnuzfYQzZnoYEy31XUTblyCFvaY8Ma+YbZwjMLFeyZLaCQ9jB5y+A7TL5/I1+qmyCSRrGXNn4qk4/FCMk4dXWSzK3U8CuffNB+WMym1Pc25VJ2gbSlan+6vNMVSj7QbIyjDKHkYlmAM8F2Mow1owkhYEdIFWsmknFCNiUnRj9Ntm7IGOF+Jk42eKeU1YMafhd0Loq2kQ8fLbRYMbAp70yoYZ2avcg1rAtnU3EQkSUnepm8PL7zpnKunrEOkyfbb1AKyeTZoC57inHxfdupJDaXDNg8lm6bmBqWrAHI+9NsCV854u7DZ3D59nFM5lKYzqcRo3Ca/Hgmaa+u/LJey0rgwDNny237rhlNJGJS3vLKNdlUArNjaSRi1MbkeYUyiG+OIYRwEZ1Gs3sf4A1r5I8ulNFsCezdLJm8+hAxw7lwJg8AAzlfS3UThVQCRNSbXGMlPvGFDVMxbv8zi7j9e6fxwNMLAOSNKQQsJi8NxmKfETZBck2UIZQVa9kKAFfukA6x7zyzGPh53jcb2nLdtKWDdDLmG13B9YCSfTF5uR910leNQyCTtxju0YVKqEm+2RJYqsioKAA9VfzkVUM+JX1AneQaPn/MiDM+zP/UchUzhTTG0gm0BGC2vEZekpB8DxMRX68VD5NfCcHkeYLYPJ5uY/JGU9iGv1MG78nlKvZtHgMAxGOEmUK6a6w8l4rg1ZWfkZdMPo49swUYTWHLe4yaVf3RKyVVDRPZZAxxK5rKj8kn4zGk44OHUEqjLn0WKcsf2Y3Nb1gjf8gqgCWZfMKlJ7ImP5lLYqaQHojJl+um7ZhKJ8M7XtUQSvndeNeLyw8Hs1+OAtiUT2EiK2/OfiNs2oy8HV0ToSZvOHLN1okMds/k7dRzPzCDY0OrnutsMt5mUJnFpBJOjf6emLwt16hMXtHkA5gsM/lmS+Do2YrvZ1SsVA20hLxuAHrqvGT7gDIhmLwt11hG3kfHPrlcw/bJTJtUoBYDA6Q8FDYZqptc00lS5O9uHsvYlTNtJm827fFkOpCQaqNlkxRAThhh5JqJrMPk/cIoK4b0Ke2ZleTQu3KrmU4xOG8yFPuitk5k2sK2G6ZwOV4Hye3g+6EXyXLDGnk2hLtn88il466HiFlTLpXA9smMS4PtFaW6aWuW6UTnh06FI9fI2TYV7y71lD1GntOvpxS5ppPG3QkrNQMJK9IDUIx8xI5X9eG7ce807n96IfCmZn8AP6BFhcn7xUkbTSFZTB/JUNVG014FuZi8wgCDGKhqtMLo8qz3Opp8+IqfXsdrp6qnNUWuAfyN/OnlKraOZ9rkLZNDKK37M59OoBRyImLJJYjJdzLy7CDdPObneBX2de1UOrmmkAm5rUxHucZstlCqmxjPJhwm76vJm5LJb/aXGmtWgxZ5nt2OV155bJvI+Mo1g2jyavVZPh9pS5MHnEipIGxcIz9XwraJDPLphGRKdVWTl9puKhGTJ31pELmmabPLMIaa4cQhy4ufTnaXa0oe7ZeXlJvyDgPpNyGKHU9E8qFOWN7+qMsaqA/fTXtmUG408cjxJd/Ps+buJ9dkPQ8S4DAWzvYDwj8wp62iWDOFtKuDz3yxjk3WBBoUXVOqm7aRDKPLM0u0o2tSvTP5fDretX6Lrclb59zPUXtqqYbtk1nboPL7PDkmYvK4xtI9MHmWazzaO8fjd3LgzhXriJFMeLNDKHl1YbbskOggOZHDRt1GvjOT50lnIpu0fWt+TL7caCKbTGA8k8TmsXRbMh8z+Wwy3lZqmGVKaeTdHaIMq4ZVPEaIx6gnI/+p+47i6j/+uh32yxNi5lxh8uzck3qie/mUtWfW7MByTSEd3lAzVMcrwBNEFyNvGYJDcyW76QQgGSHXWO+3O5Rap52RTQWHqfUKfvhyCpN//u5pEMloCC+MZsueCPkBVeWaTLK9MxCzWrUeUNjrwRP9tbsmUTdb9gQ6t1K3fTdBmnSpZmJTPontExnf+GkveNvMGrOpeNdkK3tfddmzNZ2IW40zOmjyDY8m72HyxZqBYt3E1ol2Jm945BovUeqEZxW5RjVmdnRNF7lmupBGPEbIJNxMngv6AWh7j2E0Zb0il1wzlsbZct2O0vJClSqJCFO5lL8m3zDt7k17Zgs45GXyVs2YTNIdFFBtqEY+i7rZcvnODMWhnIqH9+s9dHQR777tAAAn/NeWa5Lhic6GNPJCCByeL9vamR+TZ2OxbSKDYt3suwl2qaY4A3toVOF1vIaZIPgYlqsGFsoNh8nnUkjEYxjLJPpm8sWaYctOjF6SdLqBWXdGefg25VO4bNu4ry6vPry81C4q0pif9NCPHslgye7a8zcBcJywc8UaLpyRZCGowXOpbmIsk8SezYVQco3qSwF60+TLdcfQdPPjVNvkmphr9cPkZttExk6pV52cgEeuCWHkhRCYK9YQI7kNp+WjcDT5Dtt51kqEkuN119vh/gvqMXllLq+zGQBmxzNW3L4/AfL6ozblU75kqVxX8jw253HYIluMmiHruGeTcRhNYU8qqlyzfZITohz1QD2usLHtZ0p1/Monv2PbMR4vPxPcUwEYUSY/V6yjVDdt7YyXw3xBKuryadI/djUsSsqEkfJo8q2WwN/ceQj//fbH8d9vfxwf+rcn7QvfF5N3ab9lLJYbyCkp65O5pEuT/5dHTtlVOMMcB09WDNk4JJqyBvzw5ZSHDwBu2juDh44utT2sqjGaV+WaVAgjn/RPLLnz4BwOBBQa44S4a3c5Rr5hMa5dUznEKJjJr9QMFDh+2vPg+0H1pQDyPNcMpzF5zWjiY3cf8a2g6fYBdY6uqbXJNe5SADyxbZ/MtrWg9DL5sZBGvlg3UTNauGBaEiwOza00mvbxdHO8spF3QiidZCgnusZ6z/PMeI8ZgL29IF1+xVPSYyqf9GXy1YZpFxHbM1vASs20w3t5364uXNbY1BXsVitWXlUPOBkKkOe7mx0QQuDXP/0QFsoNfPgnrwPgTFT83YyqyY+ikWetzJZrrPAwNhwVD5MH+jfy5YaJMYXJq0bl0HwJ7//qQfzDXUfwD3cfwQe//n08dmoFgLvLDRBuBi/Vm7akcni+hIWykzUJSEa/pMzov37rQ/iLO74f6jiKNclGVcgWa9EweV4RqMtoALhxzzQazRb2P7Pgep0f1kSMbLmmVHNH17Rp8jaTj/sy+Xff9ij++MuP+Y7v5HINU/kULpiRpX9PL1cxX3LC+fKpRGDGp2TyMuqi3GjackUQFisNZJIx+1zkPRU/7zw4j3ff9igeOLLQ9t1S3bQ/n0mGTYZymLzqqGSJauu4E13Tpsn3KNewIeUcFDY+KvkIkmsaZgtHzpZxvjVBZDx+ApXx8nttTJ6POeWYLtvIB4RRtjF5q7SBilZLSHJo3X/nWSWiVUZuyzXKKqNhtmC2hH0NOAOXs38B1uTlislrQ/xwdKGCuw+fxW+97CI8f/cU4jGyV/C8EksnwmfQbkwjb7FXvtF4ectL4rJLk7eMfB/OVyGEy/CkPclQLAHd8tbr8KlfeB4AJ4697mHy6US8axJEuW5i35YxZJIxHJor2dmujImsU7/m8VMraFo1tcOUOijWnMmKIeWaaDR5J9LDvY/nXjiFZJzadHn+/I5NWcwX62i2BMqNps1isz7tCfncpwJSxFeqBh46uugrQZ1ermHreAYz+TSSccLJ5ZrdoWjzWLqjkSvV2Mh3T/ACrGxXZXLOWfcn9x1+1tqvX40XV65Al2guXoXxZJL2FPU6tVwDkQzr89ZPaij15AE28t3vBTak+7Z0MPIB5/G7VjmC5++eto6vncnz85K1pRx/ucbF5Mc716/xGvmpfLsmXzNlYh4zcm7Gra5KamYTaaVpes1otklmvBJVpT+XJh8i14Yz9K/cMQEiksUJrSRIO7pGyRUZTSY/X0YhnbBncGY+/JBKB4p8bct4RnZs6YPJ161Z2pFr3EyJMwTHMglM5txx7E4kSEz5bueHiBnj7hmp/XIlQ4ZabvjASbliOFtu4GCImiqqDMDwM6T9otJof/gAGcb6nPM24Z7Dbl2e97trKgezJXBiURo8dnL7Ol5VucbnBi/XmzCaAg8caU/AOrlUxfbJDGKcsLJUdRJzxjJtYbgqipZfJii0zgvvdWPDUbZDRmvWmNrvyV7kmqqizwLSWeli8stVzBbSvsWxWFbkyXIsk0Cj2eoaPcbhk5yM5DXyM4V0oP/rrkNnQAQ8f/cUACAWI1dmsx+TDzLyLk3eKoPBk6cXPDZumDOVT2G5argctTzBsVwzZl0DNYKobrSkJp9yxuasLOKu3+oK2TA9jtcu55gn/60WQZ3IJduYPFehBGQcfidsUCNfwp7ZvB0OaDN560JV6o5GlozHMFtI20akF/CkMRYQJ6/GNHvj2L1yjTdb9hc/vh9f+d6ptv3lLWNyeN5i8jlHYlHLDT96YtnWLTslHAHWisSaQFSE6aMZFrydnEeuAWSNke+dWHY9MCzF7JqSy+KnrEbsTnRNuyavyjXeyIK62bTP792H28/HqeWaXVuEw9zU7MtCJyZfN1FIy7C6Qjphy4WfeeAo/tNH7mnT1r0rsJyHhLDk4Rf1pfqAMlZqf1DUCMeL83OQTrpXmvKYpaFwqlQ6rBlQ5BrrunULo/TKNRwbz793bMoGMvm7D53FFdsnbEJkj9lwVhdeJu+d6Gs+ZCKViGEqnwpk8itV02oOLrfN10at6sqrP75WPCEUXfdsE9lUzI78qSpMnu/7lCWjqExe1eTDyLanbIe5vF/lc+9WCGTwAVnbH0UmP+eETwKOYWAmVmk0bXYPyHZ0Dx0NTq8Pgh2znFI0eaVWBD8QhXTCXgpyBb6G2UIiRojFrGQoZRVQaZi447Fncc9TbgmjVJeSyp7ZPI4vVjG3UvcweacS5YGTy7jhgilcOJPH3YfbQxRVVA3pFCuk3Zp8Lkom71m2qrhwJo+WAM4oD6HK5AHg6TMy/rzQwcjbco3SNIQfGFVquNsjDVUaJparhs2Mtk1kpZFfkXLGdD5l1ZdpPxetljNBOiWUy3jwmQX83ucP4P4jC22RGmdLDbvpB9CuybMxCpJrVB+QPG7/h7jSMF3nO5Nw+zGeXanZTUu8kRiGR65hw9st2W6uWEM6EbM7XnmZ/I7JjK/jtdIw8dCxRdy4d9r1ekbJbG6YTaVAmX+nK688wtg8lg50vHpzRPyyXnklyoTRZvJKfZ4qR9eknLHZvihl0pEhswqTt5KhgHByzanlKjblkvZ+JnMpW66xo2uScTsHZ+SSocp1EyeXa/bSGVCMfJ01edPWQQEZ4fHUmXLP/V7VWvIAlDC0luv9Qjphl2zli9EwHX0RcDP5oLRwZnF7Zgt2k21V253MpdASkikePF3E5dsncOOeadz31NmOupw9GfnINVFp8l4noApmOeq++GY9f9pi8vNuI+8NUwPceiQX5GJtma/9zk1ZHDi57Cr/cMqOMrGM/GQGp5dreHalhul8Ggnr2vnJNZy7wA/9ntkCHju1gnd84jtg/n625DXydbu7FeBo8jaTt4y8r1zj8QEBwUa+2mi5zre3ciOX9JXbspychie6xkqGYjLRLQ+Dt8lO/HYjL+PEvWz1gSOLMJoCN+2Zcb0uJSln4nF8WPK3l4T4afKAdHgGlRuWteSde9+vfo2XyedTCcTIYfJCCCW6xhmbI9c428+n4sGafIgaVqeWanaUDiDJnZfJZxIxJEeVyTPj4xh5QNXkZb9ItVAWANxo3VhehsdotYSrkQSjrGjuANrC0MqeSWAim7RTkNXEDkA+ZHxx53zSwlstOW428oypgmLkrdWCLBUgcMWOcdxoZ5UG9yjluOW2ZKhkwpW59/ipldDJRfPFuiuaoZNcww+On5E/L5DJt4fQqUtVwP3A8IT78su3QgjgXmWVxLVEbLlmPINGs4WDp4u2X8db5I6hrtYAWUJ5odzASs3A77/qUgDuSIpqo4lyo2n3aFXPCR8/G6PTHg25ZTmfVblGHrf/RMw9dRnphCPvqCV9AbRp8kbTvdKcsqszdmHyK3VsHssgHiOMZRK2BLdSk9Va+Rx7JZu7D51BMk644YIp1+vqiq3RdDJeYzHyLevgTQBjbB7LBMs1Nae3MaAweWVC42edr1UsJhuccxav0RRoCdhlDQCP41UZTy6dcMmgap18lZgE4eRyDdutVScATGZTtl3xLWswakaenV5uucaJrqmbMh45p8ysl2wdw1Q+hbt8tFoA+Oqjp/Hyv/w2Ds25DT2nZztM3h2hUKqbrgYWm/JO9IvqRALgKlPMxlFl8swix9IJXDiTh7Wy9DB5eaP+h6XBX7F9Ai/YI5e/d3fQ5YseQ8XIpmKoGHJinFup4VV/9e/4rc8+3DUOHAB+9VPfwX/+zMP2/5UAhgUoTFZhyk4DhhTGMgnbyKshlOrnAJ/cg0TMTjpjo3LT3mnkUnFXNA+v4Fif5tyJx06t2ExXJiy1G1OnYJg891dY1TXf92NX4YV7JXlQjTz/PaMweTUwwGy2cLbcQDoRw0K50VaXHIAj19h9WwOYvOGuFaSWCVBL+qrnzNHkBRKWQQVgl0UO6n/KmFOSmcYzTt7GStVwyZZebf+uw2fwnF2b2mQWDhNttmQmq/rM+AUGeOv1MDaPpzFfrPtGmnFxMobD5IM1eUBec57EaorDU703Kz7kRjJ5uT0hBIweNfnTy1VbWgTkc1+smzCaLVcylJd0BmHVjTwRvYKIDhLRISL63UG3d2iuhHiMsMta5gPuh8jW1pSTHosRXrBnGncfOutrwB6zIlUeOrrkep2jZwpty2fHsKiGczKbckXXpOIeucaq5+0n16jSUDYVxw7LEHk1eUA6WsfSCeyaymGKs0oDJjCgnY0ycqkEmi15Ez5yfBktIVu4ffSuI4HbAuQSdv8ziy65odbwf/gA5/qoqf2qvLN5LI0TSxxd455QVUbkxAhbTq54DIYVWcDnbzKXwg0XTLmcryzXOJq8/G00BbZYTDeo1C4v11nqevG+Gdz7rpfi9c/ZYRtyVa7hrMuZACZ/ptSAEM5koeZv2GWGlRBK9bi9qHpqBakMU3Uqy225mbyXhHQq3KVirugkM01kk/ZqlA1pwScqZanSwKMnV9qkGoD9CE2bjaqrX37PdcwBZGLzWBpmS/jKTV4j75TtbmfyeUXmHc8mbYKkauHqefabdNQyFk4NK0WT7xBdw4X0tk+65Ro+jrrZQjxGPVW1XFUjT0RxAH8N4EcAXAbgzUR02SDbPDxfwq6pnP0AAM5DUWk0nRnZY9Bu3DON0ys1PHWmvcAUrw4etYw9w2scvRqpWj4BcIc6NUwnew+QBonredtyjcJ27GYR1kPC0QtTnhBKAHjmbAWXbR+3l9o37Z3Gd55pzyq1j8NakXiTodSEkwMnl0EEvOTiWfz32x/HfR6nsIoHjiyg2RIeTbO7Jq8yeY7xziTjtqSgHn/WR6pQQygBIJkg22ip1+qmvdM4PF+2o1dOLdcwU0jZ98w2Re9UmXyl0Wxjgt5VEBE5oW3ZJOIxcrXBO2sx+em8wuSV+5NXcVfvnLTG5viJSp57wNsD1Qvu78pQJS61pC+gyDVchbLlJiFZq09sJyZfM5oo1kw7Ln0im3Rp8hPZpL0KUeWae586CyHkfepF2vIj2D2R25i8+9grHeQawD9WnhuGMDLJOPKpuPv+taNkVCafsCcxvgaZRMxVptvvvlcL0nkzi9Ndatd4V52AE9+/VDHs/q4AhiYZ6rkADgkhnhJCNADcCuB1g2zw8FzZpccDTrRFycXk3Ub+JluXb2e8bOS9KfGO5u5U+QOcC97O5N1yjUuTTzoXxE+ucVYNTlo1AFfG66TCRpgJAsCNe2d8s0oZbKi8IZRsfKtGEwdOLGPPbAH/883PwflTObzjk9/BOz7xIN7xiQfx375wwMUWWApRY42rhoyMSMTbbynbyNXb5Zp0ImYbWnn8bj1abQHYSZNXOyqxD+bb358HIB8cdfk7nU/ZD4ida8EJSx7mWArwZwByhTiVT+FM0TEWLNeomnw6EUOMpCTAxvfq8ywmr6yGnGNwyhSo56rVEvizrz5hF6vyltxVP6+W9AVkZnGMlIxX0y3XEJFvkpAKHvusi8nLMa9YhpSJhCrX3HXoLHKpOK4+b7JtmxkrLNkw25m8X55A1WjaFR1VBLUBFELYTbxVbMqn3NE1nmcdkHKUl8l7yxr4+aJyaafdo3eFkvQ4Xr/8yEl87dHT9v+nPatOwLEBy9WG3d8VwNAkQ+0AcEz5/7j1mg0iejsR7Sei/fPz8x031mwJPH2m7NLjGfl0ApW6aT8oXgfg+dM57JjMtoUbms0WjpypgEgyeTXmuT2E0tLkm/5GnssOtFqizfGq6meslzZMR2NTOwIBwCuv3IrXXL29LeOVccWOcftvZoXff9Y/SSdIk3dkBBMHTqzgiu3jGM8k8b9/+jpcOJPH4fkSnjhdxD/e+wy++cSc/T01Lp8ntWrDdDkBffejPLDsNIzFyDZEccvZBiiavA+Td0pFxF3XApD3wWXbxrFvcwF/9tUncGq5ilNLNRd7j8UIWybkPmdZrvGZiFznzsfIA3LCUJk81zpRNXkishy7TTxrGd+rOjB5tayBetzHFiv48J2H8Yn7ngHQQZM3Wq6SvjwGVQ9WIz4YMt0/2Mg/65k4xrOJNibP56molBs++GwRl28fb9ufHHPcxeS9mrzXyNc8EhWDE6LmPUa+VDfREmgz8lP5FM4oRp79MZmEauQdx3JNWXnakT+Npm9yVj7lFP7j8x2kyf/NNw/jfyilSex6QxPtcs1i2bD7uwLDw+S7QghxixDieiHE9bOzsx0/e3yxgkaz5W/krWbefo4QQN7kN+6Zxj1PucsAHF+sotFs4bkXTKFqNPH0GcdQyjoicVesO+A4r0pKxUBAXoyWkGF3alNi+V3HaavG83qbH/NDct35U/hfb36Oi7Ek4jF7OXzFdofJb8olkYrHAmt3eLfN4Jvl2GIVp1dq9upg35YxfO4dN+Lr//kHcMd/fjG2jKfxmQfkXH2mVMcTp4u4ZKvMeGQ2JIs0+RtCm8nWvUZe7p+X2oV0wo5l5tokXk0+ZYVPAu4HxjGQ8np9+KeuRc1o4h2f+I7MdlWYEeBINrZck2qXGYBgfwZjdiztKmJ1ttSw+7OqyFkP/txKHUQy1HNTLunW5D3XydHk5TGyFHiPRVTUEreA48eomU1XSV/7fSWZz+szAvzT/VV4JSA/uYbPk8rk54t1W+LxgkMo2beijilIk/cz8rxyOltyG3lvSQPGTCHt+mylbiKnPOuAR5M3HVmGI3844zVGcEmzuVTCvte9MpTXyC9WGnhyrmQfp11vyBNdA0hC5WLynCuyzu3/TgA4T/l/p/VaX7Ajazbn297LW828vWGNKm7aO4OlimEXEVO3+frnyAXGgRPOe2VPKYB2Tb6JgqL18Y20XDF84+QBR67hz7Lmp8oNnTCRSyKTjGG3MtERkYwTDkgGKdUly/YyKTYQ+61CWZcrEwcjEY/hDdedh28enMOp5aptYF591TYATqxxxdMVSgURtTXOUB9WNrTqsXulCkAyVLefg1xyTTYZt+WivZvH8IE3XI2Hjy1ZNdUdZgQ4mqcj17SHeQIy/JSoXf5jeJn82XLdJdUwOHpnrljHVC6FZDxmJ2UxgnxAfA6YZX/vxDKWK0abJq9+Xo2Csc+XUirbbIp2Jp9vL9ylgkkEX6+JbBJVQxbpWqkZmMglbUlQLTc8t9I+FnvMVjIUR0klE24m3x4n3/K9zwpWJy1vuWFvA3vGdD7lcpiXG+0kZSyTQLEma+Y7oZsxe2wcXaNmHQNW43arKq6ddJZoT4YSQvq1mi2BJ6wQ7lMrspCeel25js5SpWFp8vI9IgoVd7/aRv4BAPuI6EIiSgF4E4Db+t3YYauv6+6ZdiafS8lSqd40YxU3WuGGqtzARv7ll29FOhFz6fJnLFbG8BZ5KikNRQDHMbpYabTLNdbfxbqBxYqBfZ4qft7EqyDMFNK4fPtEmyY526E7TrFmtGW7Ao4kcr/VOPxyRQJS8Z+uPw8tAfzz/uO4+7CM7HnxRbP2sQLt+rAXuXTcw+Rb9o0869HFAf/OQNzEm5FKxGw9Uu3gxXjlldvwSy/eDUCm26vYuSmLeIycfXNaf5tcY6CQSrgYnorpQrpNk1ezXe3jtyp+zhdr9j63T7qbPvMk2B4nL4+R7xUhgHueOtt2ztXPq1EwDLWzmdFsuTR5AJjKJduYsIq5Yh2JGNlhvRx7fqZUR81oYTyTsEKKyWbApbqJcqPpcq6r4MqZDT8m79PMXa3droJINvQ+E8Dkxz1MfrogG41wtF214V6VA9LIt4ScAGoeWSaTiFtlDcy2ony5dBwtK5nR63hNxWMy5r4la/HztWW7c2qp6nK6AlI2isfIjq5RZdFknLpq8p0tyoAQQphE9KsAvgYgDuCjQohH+93e4fkSpvMpV1gho2A5O5ykhvZD2zyewb7NBdx1+Cx+6Qf2yG3OlTFTSGMqn8Kl28Zx4KQ82UuVBr795DzefIOzEPGWa1UbigCOdrbETD7ezuS5hs6+LQXsf2axXa7pYuTf8/orXJMHY8t42s4a9aLo0xUKcAzpw8eWcMF0zhWBoGLXdA437Z3GZ/YfAxHwvN3TtqHiWGNvApoX+VTCpclXA+Qahl+BKsnknX2k4jGXMSmk2/f//738Yly2fRwvu3SL6/WfvelCvGD3jL09h8m3yzVBejwgJ92qIaO6cqkEzpYadqkG7/GX602UG050ytaJDPY/45Tb8N4D3pBdlmviMcK3n5yH0RRuI29ntcoVwxWelZnauKbhp8nnU1ipmb56PSDlmplC2p7weDXKjmDuvFRQWglypc8t4/5MPmNVzrRljQS53/NE18iJzZ+bThdSLukM6CTXpGA0BVZqJiaySV8mz8/DStWwk/LcTL6FGLnLHgNKyLBVihhwa/KAPP+qNPaoZXdOLdfskhEMIsJENonFisyrcD0DCtEJwqpr8kKI24UQFwkh9ggh3jvItg7Pl1zlDFTkrDhnJ4TS3+DcuGcaDzy9YJ98LnYGSGfmoydW0GoJfOGhE2iYLbzxhl32d9WHrmnNxHmX49Uy8tV2uYb/Pm4ZefYrLCtyTUJxPAbhih0TuGjLWNvrnTL+/CpQAmqYYguX72iXalS88YZdOL5YxbGFKm7cM92WNdhJrgHa63mo2ZpqGKN3bOpDroaPAe5IBa+0xkjEY3jdNTvaxjZTSOOF+5y4bWZxJU/9Gr9mKyocLViehzOlhqukgX08iibPDHvbRBZLFcNmq6Wa+x5wJCu3Jv/83VP4t8fn7O0y+HyW602cLdVdUUuAOxrJbApfTV7djxdzxZprm8yOj1pGnv8fyyTtCUut9OmHdEIyW57MuR4LwD0FfDT5gPvMq7MDSsOQTLsmDzjRUHKS9jJ5p9ywEw3mRNpVLbkml/QPaChbCUzyuNzlGhrNll3nKhEjWyZWC+mp4CJldbNlR+oB7dE6flh3x2s3PHBkAR+7+wg+dvcRHDxd9HW6ArBrj9hMPkA6uHHvDKpG0y5Ypk4cV2yfQLFu4uhCBbc+cAxX7ZzAZdsdCUMNg+SltWoAJiwHyXKlgXoXI7/PMtQrCpPPK47HXrF5LI3lquFbmrZY8zdUKnPxsj4vfviyLfZK5aa9M22xxt3kGq8mr35+LJ1AJhlzhXiq9UEYfhOnGicfpJuHQVB0jV/1ThWc9HSmJLMtF8p1VyKUs/04inUTZ0qqkeeGNvKe4IlKrSoJKEy+2sBYJoEX75u1SyK4NHnr75NLVbQE2uQaqX8r0TUJ973ml+5/8HTRfv4Oz5Vc22TDecy6p9nIF9IJO4nMm5TlBY+fV2TJuJfJt8s1QfeZV2cHnOdrIueVa9yTc7nevhJlHb9YM+xgCx4va/J+k47q33EKwbmZvGG2bF/Otbs24eDpIpYrBparBrZNtk+IE1ZxwrbVbIiCZ6sq1wwKs9nCz//DA66koWt3Tfp+NpeWVQQrDVlqwC9eG5DNpWME3H34LPZtGcNixbAnDo4u+dT9R/HE6SLe+6NXuL6rRjv4RV2oSQte/Zi/e2xRsh6ns44qN/R/Ofghmi/W7XowjFLNxMxMu4SgPixXBOjxjEwyjp963vm4/cApXGQ1jFBjjbsx+VzanXxSM1o2UyIiXL1z0q5RDvg3cq57E8w80TXbfR6OsMgFRNfwcj4InPR0ptTAYqWBlkCAJp/AyaUqzJZwMXlAsrfdswV8/9mSHQoIqI5Uh8lP5pK4aa+zAnHLNfLzzKxnPew5HXeHUHp9GH6Fu/7giwdw39NO/sWPX7fT/pvPy3FFrgFkdFDRI9cEOV55zDwpqJN4LhW3y27wxKdGZXmh6uz8+YVyA4kY2c087M/mOVvZYfJbPRFAfH+u1AwlhNIJ8a0ZstGId9Lh56DcMBVN3nK8xhUmb02mL9o3g/uPLOBbT8oQcq8mD0gmf6bUsBqXuEOzuzH5oTbyB06uYKVm4s9+/Eq87LKtiBO1zcgM7uzDRb6CMJFN4sodE7j78Bl7uc5yzb4tBSTjhI/+x9PIJuN47dXbXd9NKXKNXxRPKhFDPhXHYgdN/vhiFUTAlrE08qm4S64ZyMgrGX9tRt6qh+6FapT9Imu8+O0fvgi/9bKL7AdoKp+y0+DVPpd+yKcStnbLn1cfjs/80gtcn4/FZFx31WXkm22avC3XNMyuTutOYMerN7qmVDOwc7J9+cyYGXOMhV3SwMeg5VOOvsyavNr0+ZmzZdzz1Fn89ssusr/Dse2OJt/AZFaWseDwRbdc4yYSbXJNIubEb/tF1/iU4H3mbAWvuXo7/ui1l4Pg+J0Ax6gf9Rj5sXTCXmnMF+tIJWKBE2W6jck7YxrLJCAsxyc/G53uM1tnr5q2nTi9LMstex3nM2PWCkwhKV6J144UUuQa2/GajKNYM9ESou3Y1DIeRssy8gk3k2+YLduf9aKLZvHnd3wfdzz2LAD4yjWbcik8OVeCEO5Y/qHQ5AcBR8G89NItmMqnAg08IE9s3Qrl6iQbAMAL9sjm0ly5kZl8OhHHRVvGYLYEXn3VtrYyAGrGa5CjlGs/e51XtpFfqNjlbcezTgEkb8x9r2BHqF+51ZWa4Ss5pBIxJGKEHZNZV9JVEIjI9bBsyjlMPijqgSH7ybpr16QDHGj2d5LuTkdePdIVXVMbzMgn4jGkE7G2+jXd5Bpm7WfLDSfbNe+nyTvbYFbLtd5PL9fw2f3HECPgDdef5/peRulDsFSVTD4WI7zAaqHnzniV5+bYQtW1ffV9tTNUyiPXeOvX1IwmTq/UsHe2gCkr4EGVE1nO4EnFNvKZhEuTny2kA2VIZsYsq6grtTHF8ckIipMHFJ1dCWk9udwerQI4hf+4x4Ff4IDb8dq0a8YAVsMdK04+4/memmRoZ/J6Ha9mC4vlBmIk2/yNpRO400o49BvvRE5WuB05Tf7uw2dwydYxV/ZgEPgBny/WuxrLm/ZOw2wJ3Hr/UaQTMbsYGOBo02967nlt3+PU8EazFZhgNGldjCDHa7Fuugo8OdE17pj7XtEprbuTocom47h8e2epJgjM5IUQIZi8u8Z23eys4fPY1ImhLcHMU2rY28O2V/jVlA/yZzAyyTgK6QTmi3Vb3/XV5JVzw6uuTDKO6XwKxxYr+Kf9x/GSize7kmAAJ44ckPkXbEi5Doz6wCfictI+aWn8s4V2Jq/Wk0/E3I+/k1kpj4OLxp035b+SSSdkbXVubM5G0SXXeJy1XjAr5bh6lRiNK45Php9RZXh1dsByZPqsxBLxGDblkrYuXq63+3ScFoAmaoa7dj/X3KkazTb/XydNnn/XzRYWKg1syqUQjxEu2z5unwPvPQDIhKiildHvDj4gex9BGFq5pmY0sf/IIn7yeeeH+jyHz80V611lj+vPn0IqHsOTcyVcum3cxU7f8rxdmCqkcO2uTW3fc5bPLUeuSbUb+YVKA2ZL+Bp5wAknG1eMfLluYscAmvJ0Po0Yoa07TqUhdcOgc/IbN+9z1cHpBZLJS/9DsyW6xMknXOGJ3Zg/YDXB8JQ1cLEYi5kazRbqZrvG3CvYr8NoWjX+O4VQAtK4uJi8DylRC+apRm/bZAa3f+80SnUTb7yhnVikPUyeJZXXXL0dB58t2iUtGJlkHKW6KbOgPZFaKpM3fOQax5ku70mW1/xCQhkT2SRqRh3ZZNzeXyGdVEIo69g92568qO4T8NfkvX1WWy2Butnq4Hh16+xCCJxaruEVl/s/VzIap2HV32+1hVBmrGNaqXHimZKoleTqmaLd8apo8mwfkkoVSoCja5w2kVfsmMB9Ty+4Cump4AnYmwAXpnTx0DL57xxdRN1s+Vau8wNfoDBMPpuK49rzJwE4DlDG1edN4v9/xSWBy8t0Io660WwrQ8yYzKZsQ+ttGsJQ08Lt6JoBo0PiMZkM4i1t0K32yi+8aDeevzvcOfZiKi9D5Xii8iaFqMinZKcnLrdc6/CwMjIeJu+nyRtN0Vait1/kPY1DvLXkg8Che2dLDcRj5Cok52zbiiTKuEsebB2XPVE3j6XxQ5dsbvtexoqIabWE1OSth30yl8J7Xn9l2zHbYak+IYuqQWj4yDUAZ71KJsxG3uvjUcErC1WXVpuCy6SsYPLCrHQlQJMHnAlALS3gBzXSCZBO14bZ8mXGAMfV1+38DT+7MW6tSrzx6Xxv+kXX8P+Vuhr/b4VQKvVmFpSG7xz4EDRW1RfSFka8UTX5uw+dRTxGeO6FU6E+z8Z2uWoE1lBRwVUpvRUtu4HZUMlTZ5wxmUvaRZLctWucv5nJjWeSrrIG3RhjN2web896DSozHAX4BuW68h2ZvJ0gYsJoygYRnaJxAE6UcW7g9npA8m82SoPKNXmPXMPGpdt2OXTvbLmOqXzKNzuWmbw3yoSdrz9x3U7fiDCukVIMKLTV/nl3qQjve05ZA/+EJ7V+zbHFKlKJWJvso4IlFbVsABvns6UGlqtGYGQN0O54VZ8TDsnk0gROqz3/+4ZZMSdEeRtiezFtMXmnkmT7deZKlHWj1cbkK1ZJB+99n/NJhvLV5CsN2zfAMnHQWF3Nz5XJJr2Rmfxdh8/gqp0ToY2Tq9RnF+MBSI82AFyytTc9mnVN1pe9s/9kLmnPrO4QSsXIezR5IQRKjcGia+R2M21yjV1meMBt+4FvUM7i7XTe1cYZNbv5R3fHa60RLNekPUZ+YCafTriSoYL8Ll5MW+n088WGb/gk4ORteFnt+dN5xEiWjvADM3lu/6Y+7H7g8zPrY1jdVShFmyYPSEPJ5/Po2QrO25QNLOkA+DN5vo85A7ujJp/0hFB2YPJ+FR9VeHV2x8gHyDV5yeSDKtfyGFas/BNv7X7uP+T9XjxGyCRjFqEJyHi1omuYKO2eLWA8k8AF0/6rJnV1mPE4Xte1rEG/KNYMPHJ8Ge+wSg+EgfqAh2Hy15w3iS++8yZc2aMenbY0+WLNRCoea9PPuGIc4GYliRiBSNYdmVXkmnKjiZWa2VE3D4vNY+m2Xq9hDVU/cJi8NPKd4+QdJp+IUdfPA/JmVgtOeQuU8YPDGvIg0UmA9OtwFUDAKRjWKboGkDLBQqXhqkvjBYfnedP73/LcXbhxzzQumPFfUaatEEpuEO8nBalgR2aQXMNO3IZPMhQgJ+5Dc7Ke07HFSkepBgiSa+TfT1kVXTvLNcHJUHZ0i6eme6cVo1pLyG7AEeDrmimksVIz7XLZ/kY+iWJNhqq6jbw7M9cLTv6zjbwnhLLOTN5quxiPET7/zpsCg0zccs0IhFDe/7TsPHRjSD0ecBv5fAgmD0j9vRNL8YNc8jatrlDt+1HDPFUjT+Skq9tyjbXE5ZtxUCa6eUwmg5jKRQ9qGBIFeHnMURidM1451bvpJJb4OJhUeMMug4q+cTTIoMfobeYdVIffi5lCGkLI1pRBTJ79Ld6Su9lUHJduC15NcileLjXAvViD4GjyfnKNUxzL9Ck1LLfvhMUeW6h0dLoCjqSilg3wMvmgiU8d70rVQIzgkqwyyThS8ZjteOUGMh2NvFIV9NRyDck4YcYnpBVwHOTse/B7/sazCTu6RmXQLiPvQyq5BaATXeNOhjpbrqPZEq6mQHtmC4FynEoeg0p7BGGojPyp5Rre8+XH8Dd3HkY6EfONcAmCanC9rf+iRDrpRNf43RQq01LrcMj/3Q8gX9CTnv6m/WJ2PAMh4GK/3eqhDwK+QdnId5Zr5P7LDadSaFcmn3BKzZpWBI/X8Qo4cd2DTpIFTzPvYj3cBMmhe+VG0zeyRo6NGXb3cGAVGSuEkiWUiWwXuaaDJs+TYtVooiUQqMmXrTaFKzUT520KaeQ9jlfAqfDaWa5xmLzfeMazTjhmmPtmZixth1CeWqr6JkIx+LodPSuNvC+TT0smXzOaLlKiTjR+32Mmbxcoi7mZ/LNWsliY/BRAnlM+jLbomnWuJ98TFsoNfPr+o3ji1Aped832riF2KtTIlDCafL+Q5VpbKAZkqKoVMr0hbNw4ZNZj5E9Yjsso5BrAHUZpGyqfjNdBwUtI1uQ7XS82cu5uOp1vv4ySJept/Qc4S2BmnoOePy5yx+VnnQmy87lTk5/8askD0qF2+fZx3HBBuEACBsuDHME02SEhEOgcXcMTAK9WvKWGAWfifuSYlP36k2scJh8j/+QwZ7zWfWG19fNiTAlO6KbJA47ODsguS9sDHJmAkzzFyVyBTL5qtmnyrsbdfn2N03ErTr4l82s8jYdOL8sx+lXU9UMsRvY59vaO7tQYHBgyTf7y7ePY/8ev6Ou7snC/1LzDyjX9IJ2UpQiCyhC4mLxPnPJkLmk/bOMeJh+FXANIlnAlpK+hGBAFFAWS8RjGMwk7+SaM47XcaCIf4mEF3FUI7dZ/nhsccGqtDGrk8+kETKt1YzoRd6Jrupy72THnQQ3SVPPpBP7l11/U85jSVncklmu6Rdc45ZuDmTz7afzkGtaIHzm+BCA4EYrRyfF6YqmKzWPptt4HKrwGy4sxJbHKjq7posmv1EzUzSZOL9dwjU9fWQaHXHJZBr/tjmVkYxTZeMfteGX4rSzyqYRt5F2Z79bq3mbyXRzpKiZzsqlLe9b3BmLygyAWIzuCIYzjtV9wyFKQXOPS5OPtRl59+KKWa1jvVcMoSzVZQrXTgzYIpvIp2wB1dLza9TzMUIxMvu80cm7YTL49jT+y6BrFbwBIY0jUfWWoMlW/bNdBkLHkwaWKgUI64StpqPD6ffze4+Pz2xYz+YePh2Py3KdAlWtUQtFJquExxD0s1739pBMnH0KumS44YZSnl2uBTlf5WTk2lmt8mbx1LGdKjWBN3o/JW6tCmXTmPHs2k+9RrgEce6HuOxmnjRsn3w/4Ig0aZdEJHKFQDIhrd0fXuA1rOhl31RNpM/IDsm2OZ1YTogatbtkN6nLTW1dbRd7W5Jt22dYwZQ3MlrAyWtvDLpO2E6thdSQa7Hbm+4flDC5p0K3880Q2aRuqTtJEP0hbq5mlaqOrVANIJ+BYOuFLdNIeJu8n17DReeT4EiZzycBGMgwO6VRXsOlEXPE/dc/i5kqUftdvLJOwo2uqIe4bXkk9+WwRjWaro1yTT8mm3KcsgxsUXQPI7Oeg6Bq/78mevs22YAE2+M9a4Z1h5RrA6VfhXv3E0Wxt0LIG/SCfTgDF+qoz+brRgtlqtZUvBZxKlOVGs83x+q4fucTFFmxNfpHlmsEmp1Qihql8ysXki7XOBbYGhbrczKSCjayTBdgLk5fv15Q2ad6lKiA1+SgmMtvINxwj383IAXIVOZVPYb7o3991ELAmz2WGu+FnbrwAL1aaoahgw8uTmC+TVxqHhAkvvnbXJH7vlZe6GrAA0jifLTcCO0KpSCet56ULkw8j1/BKitvpBWWQAk7LwBNLVcQDGvb49TjwjsHvPs5ZneoM0y3XJOKyqX2xLkOwe5GWeUJ1MXmfMFgvRszIs1yzipq8lTVYM4JLGk/mUig3qm03LfdFdbYVQyoes5duURiqzWPpNsfrIIXPuoGNQjxGvpoqI5WQvT8rhhNCGaasAQC7WTTgn0W8qBTuGgQOk2e5xgh9TWYKacwX66GK6fWCTDIOIWS5jskukTUAcPHWMVy8tb1zGOBMkDyJ+V0vlZF3C58EpNH6RauProqCZeS9Ne390JXJV91MvhOZ4JXU9ywj34nJA3JSOLFURS4V912xqTJUJhGeyeetTmB+rRRTVljspnyypyZBQY7XbhgpuYYZ/Ooz+WZgSz3AuRh+zEQFEWE8m0RLQPaK7CGaKAizY2lXueFSzViVbFcGL++9Hev9kEslJJNvhIyu4faEhiLXePRIQJayiITJK23bgOC2iX6YKaRQSCd6iggLg7Si4XYqtR0GvLL0q93OSMSd2u87uzhdO4GvR5iQUT5nvkw+Kx2fRlMSqxh1NmzTNpNfAdCZycvPy/EFEUM3k/fX4f18BDnL8Vo3Wy5NHnDGv6kHpyvgRFZ5k6G6YaSMfGENNPl0ImaHJfo1jgacixHmAkxkecz9t/5T4e31utpyDd+o3WLeAafcMJc1CBNdA0gGx5UYg0pFRCnXcLXMUg/nbvdMvmO1xX7Bx3imVO+a7dp1W3YP2GBNHnAm7jBMPgh83sIYeZ64Uz7jURt3cOu/Ts9JIZ1AOhHDiaUqUvFYYHIag+WdoOKAqlzndrwqf/sk9bENWqkZdug0g//vxekKABfO5JFLxV11gsIw+RGTa9aGyTOC4qfZ8IW5AGpfzCiweVzKBq2WQCxGq+545ZC7MBIZlxuuNZog6l67hh8kVZNPeZxOjChCRPlB5/o1xZrZNbqE8a5XXto1yqEfsAEUonfm54VXkw+6Pzflknga6JoI1Qn8bHgzfP3A90FQnDwgQ4E7NfFmqDr71ongRCiGzeQDCJtq5NM+Onw2GffdB2fBLlWMtsmLj7cXpysAvOaq7XjxvlmXfQsTbDBSTJ6X26uqySsXOmjFMNETk4/YyI+lYbaEHVZYqg1e3bITbCYfQqbIWxEHNbOFTKK7vGMz+UbTNxlKPb+Dhk/KbVjOYXa8dukKpSKTjIdy0vYK9XjDOF47wYmTDw6hBByGGXaC80MvTJ4nc9+MV5XJd+jvqoIlm25SDeB09goihoUAuSadiIEoeAXLtmipYvhq8kBvMfKAdPB7J4ZkCBszkkw+Cm07CCr7CTLMkz4OkiCwkY/CSAFOyNqzK3VsyqVQapirUmaYYWvyISZWrudR7dL0m8EdgD7wtYN2ESZVj1S1ziDprBfwNSjVTTRbAsWasarnLgzU4x3UuezEybcXA1OxKZcCEVwd03oFG/kwjmhbk/d1vCot+Dq0/lPB+9wewsjzZ4OiXOIxQiEt2xmq+yYiZBLxwPHwpLFcNdpkL1uT75HJ++Gck2tecvFm6ZxZpcQfwB3CF2Tkf+iSzZgr1kMZeWZ/Uenml2yTkRX/cWge501lIcTqlBlmbMr3wuRlg+eq0bQjKjphz0wBN1ywyXYUvmD3tMvwpCLW5NMJmZhTqTfxwa8fRM1o4dpdkwNvdxCo2m+3MsPd4M149atfDwAvv3wr8ulEqJVoEH7oks0QItxqlp8TP1bK+vMKa/IhyAGz861dImsAx8h3kni5Z603UCCbigczeYt0lOpm23HxOenmLwgDv8YvXoyUkX/hvpm2eN2okQ4hEVx/wRSuD1mjxGbyEfkR9swWcN35m3DrA8fw6qu2A1idkgYMXnKG1+Sbsg5IiM9P5JL4p1++MfD9qOUaIkIuFcfXHj2NJ+dKeMvzduEVV2wbeLuDQGXymwaUa3hbncoaAMDNl23BzZdtGWhfP3jxZvzgxe2drvzATD7tK9dwuWGjB7nGYvIh2mmytNPp/h3PJHFquda270wiFvg99XWvJp/qU5P3gzcXxw8jpcmvBaJmj1HLNQDwphvOw1PzZdx5cB7A6pQZZoxnk4hR90gZwIquqZttFf36RTIW7bXg7Tw5V8LVOyfw7tdcFsk2B4GbyUejydtyTQgWuBbge8Ffk3eaeVeN7i0jASdiJqjLkgo28p2eP35+vPdsxlNjXkUn5yhPrr1q8n4IktxUaCPfI1RmFQVDZiMfpSF+1VXbUEgn8Lf/8RSA1SkzzODesuMh9GKuD18zWqGW3d0Qi5F9k0d1jOOZJKbyKfzNT13n21B5reHW5AczCm1lDXw6Q60HeCLzk3b4GVupGqg1wmnyXDokjE9hKpdCMk62g9cP43bNGPf4xjLBZR/ynYy8zeQH9/eEkcMGejKI6A0A/hDApQCeK4TYr7z3LgA/D6AJ4NeFEF8bZF/Dgqhjs8ftOPnoDEoulcBrr9mOT913FMDqMnkA+MhPX+eqyRMErrEtl93RGBjZzDs4+7hX/OmPX4lCOjGQ0zFKqD6gQR2vdghlh4zX9QBHrPkx+XiMkE/F7eiaMOTghy/fgo/81HW4dJt/5q+KRDyGj//c87BvSyHwMzaT90wwf/qjV7qujwo1JNN7XPx/r3HyfggTQjnok3EAwI8B+N/qi0R0GYA3AbgcwHYA/0pEFwkhOhc+3gDgmTOo1kWvGF8FuQaQko1j5Fc3QiRsc5dcOo6WAJYqDVwY0O6uVyQTMaDRjIzJ99KoZi3AhiWfig/kCAWclY9dhXJo5Bp2vPqPZzybtOPkw8iC6UQcr7hia+j9v2DPdMf32ch7Dfpl24M7eqlMvq1QITP5COSaMDZooLtGCPG4EOKgz1uvA3CrEKIuhHgawCEAzx1kX8MCXj6HqU4YBrZcE7GRv3LHBC6z2sqtplzTC/jGXyg3XPkGg4DZ6Go6l9cT/BAPGlnjbC9uyzWDVu2MCukOjleAK1GGl2uiBksyvZSsyCRlHD3gL9fkOuj5vWA9k6F2ADim/H/ceq0NRPR2ItpPRPvn5+dXaTjRgR+6qAznzskcJnNJ7N3cfWnZC4gIv/CiCzGdT0WyLIwCHHGwVDUie1iZ3UYVnTRscIx8NKuxVCLW1pJuvWGHUAYYLFmJkuWatR/zJdvGsWMya/erCAMip7+F97j2bi7gihAVPsMgEk2eiP4VgN/a5/eEEF/sY1wuCCFuAXALAFx//fWdCyMPAVIRG/mJXBIP/8EPR7ItL37s2p14/TU7VjVvoBdwxIEQ3YuThQUz+dX2O6wXmO1FZeTd7ROH477oVKAMkNf21HINZkusC5N/7dXb8dqrt/f8vZzVM9hr5N/5kr1450v2RjK2SDR5IcTNfez7BIDzlP93Wq9tePBDsppF0KLEsBh4wO2MipzJD4kkFTUSMUKMEKrMcBi4G1gMB5PPdHC8AlKT/67VqSrqKp+riXwqjnmEY9v9Yj2rUN4G4E1ElCaiCwHsA3D/Ku1rTcH64agaldWEKqlE9bCmNtik2yuICOlEfOAywwyVySeGhAB0KlAGSCbPfXyjCL1dK/DK1a+6ZlRY9bIGRPSjAP4XgFkA/0JEDwshXi6EeJSIPgvgMQAmgHeOQmQN4NyQoyoPrCbULMDIjHxcNl4Zhpj21cJv//BFuPb8aKJ+Urb+TZEEDkSBTrVrAHd02HrINf2C7/fVXDGtepy8EOLzAD4f8N57Abx3kO0PI0bd0beaUFc/UT2syXhsZFk84xdetDuybbEhHRapBuicDAW4y/1uKCNv3e+rea7jlpzXCcNzpTcI0iOuAa8mVoXJJ2L6WvSAdIcSAuuFbpq8qzvTBpJruLJlmHLAg6DbtRyeK71BwM13N4doUKzhhmrkowqF25RLhqpZriGhyjXDAk4KCirAppbM2FBMfg00eaC7ZKMpUI8gItz2ay/EthC1qjXcUIs2RVGgDAB+/9WX2XHfGt3RLSZ9PbB3cwFf/rUX4vKADFKVya9mQ6CowTLiap/rbs5XbeT7wJ7Z4DoXGsGIxwiZpOxUH9WyO0xTCg0HqSE08gA6JgdtVE0+uwaOV6A7kx+uK60x8mA2HxWT1+gNwyjXdMN4QAu+YQcHZ6y2kdeavMZQgZfbGyneeZQwjI7XbnCFUG6g+4bv9TDdmwaBZvIaQwVmN1GVNdDoDcOoyXcDl+MGNpZck1+DEMow2984V1pjJMClDTbSwzpKSG9AuSabjCNuBYNvJLlmLZKhAM3kNYYMDpPfOA/rKIENQlAT72EEkezclLIarW8UcBHDKPpOdEK3EM2Nc6U1RgKsqWojvz6w68RsICMPSF1+o63+bto7g99/1aW4aufkqu7nVVd2bja/sa60xoZH3jby+tZbD2zE6BpA6vIbzchnknH8wot2r/rq42duurDj+/pJ01hT5NIJxGjjMclRwTDWrgmDsXRyQ0XWDBN0MpTGmmLnpiy2T2aHpgLiuYZOTbOHGedP5zAkjaw2HLSR11hT/OKLduOnnn/+eg/jnIXD5DfWJPuHr70cLTH0jeOGEtrIa6wpkvHYhmORo4R0cmPKNdpR3z821pXW0NAYCDaTX+WwPo3hgb7SGhrnEGxNfgPFm2sMBm3kNTTOIWzU6BqN/qGvtIbGOQQ7Tl7LNecM9JXW0DiHYNeu0XLNOQNt5DU0ziFsxCqUGoNBX2kNjXMIdj15LdecM9BXWkPjHMKwtv/TWD3oK62hcQ5hMpfE1vEMds/k13soGmsEnfGqoXEOIZOM497/+tL1HobGGkIzeQ0NDY0RhjbyGhoaGiOMgYw8EX2AiJ4gokeI6PNENKm89y4iOkREB4no5QOPVENDQ0OjZwzK5O8AcIUQ4ioA3wfwLgAgossAvAnA5QBeAeBviEiXkdPQ0NBYYwxk5IUQXxdCmNa/9wLYaf39OgC3CiHqQoinARwC8NxB9qWhoaGh0Tui1OR/DsBXrL93ADimvHfcek1DQ0NDYw3RNYSSiP4VwFaft35PCPFF6zO/B8AE8MleB0BEbwfwdgDYtWtXr1/X0NDQ0OiArkZeCHFzp/eJ6GcAvBrAS4Ww+3OdAHCe8rGd1mt+278FwC0AcP311+v+XhoaGhoRYtDomlcA+B0ArxVCVJS3bgPwJiJKE9GFAPYBuH+QfWloaGho9I5BM14/BCAN4A4iAoB7hRC/LIR4lIg+C+AxSBnnnUKI5oD70tDQ0NDoEQMZeSHE3g7vvRfAewfZvoaGhobGYNAZrxoaGhojDG3kNTQ0NEYY2shraGhojDC0kdfQ0NAYYWgjr6GhoTHC0EZeQ0NDY4ShjbyGhobGCEMbeQ0NDY0RhjbyGhoaGiMMbeQ1NDQ0RhjayGtoaGiMMLSR19DQ0BhhaCOvoaGhMcLQRl5DQ0NjhKGNvIaGhsYIQxt5DQ0NjRGGNvIaGhoaIwxt5DU0NDRGGNrIa2hoaIwwtJHX0NDQGGFoI6+hoaExwtBGXkNDQ2OEoY28hoaGxghDG3kNDQ2NEYY28hoaGhojDG3kNTQ0NEYY2shraGhojDC0kdfQ0NAYYQxk5InoT4joESJ6mIi+TkTbrdeJiP6KiA5Z718bzXA1NDQ0NHrBoEz+A0KIq4QQ1wD4MoA/sF7/EQD7rJ+3A/jwgPvR0NDQ0OgDAxl5IcSK8m8egLD+fh2AjwuJewFMEtG2QfaloaGhodE7EoNugIjeC+CtAJYBvMR6eQeAY8rHjluvnfL5/tsh2T4AlIjooM9uJqztB6Hb+2E+MwPgzIDbGKVxhBlLFPvR49Dj0OMYfBznB35DCNHxB8C/Ajjg8/M6z+feBeCPrL+/DOCFynvfAHB9t311GMMtg7wfchv79Th6G0tEx6vHocehxxHhOLw/XZm8EOLmbp+x8EkAtwN4N4ATAM5T3ttpvdYvvjTg+2E/o8cR7TjCfEaPQ49Dj2MVx0HW7NAXiGifEOJJ6+9fA/ADQoifIKJXAfhVAK8E8DwAfyWEeG7fO1oDENF+IcT1ehwOhmUsehx6HHoc/WNQTf59RHQxgBaAZwD8svX67ZAG/hCACoCfHXA/a4Fb1nsAFoZlHMDwjEWPww09Djf0ODpgICavoaGhoTHc0BmvGhoaGiMMbeQ1NDQ0RhgjbeSJ6KNENEdEB5TXriaie4joe0T0JSIat16/gIiqVomGh4noI8p33ktEx4iotF7jIKIcEf0LET1BRI8S0fvW8Xx8lYi+a43jI0QUX49xKN+9Td3WOpyPO4nooPLe5nUaR4qIbiGi71v3yY+v9TiIaEx57WEiOkNEf7lO5+PN1ucfse7ZmXUaxxutMTxKRH/WyxgiQa8xlxvpB8CLAVwL4IDy2gOQUUAA8HMA/sT6+wL1c57tPB/ANgCl9RoHgByAl1h/pwD8O4AfWafzMW79JgCfA/Cm9RiH9f6PAfhUp8+swfm4E4PlgUQ1jj8C8B7r7xiAmfW6Lsr3HwTw4rUeB2RQyRyfAwDvB/CH6zCOaQBHAcxa/38MwEv7vVf6+RlpJi+E+DaABc/LFwH4tvX3HQC6sh0hxL1CiLZs3bUchxCiIoT4pvV3A8B3IPMP1nQc1na4nEUCcsLpyXsf1TiIqADgtwC8p5f9Rz2OQRHhOH4OwJ9a22wJIbplTq/WOAAARHQRgM2QhGStx0HWT56ICMA4gJPrMI7dAJ4UQsxb//9riO9EipE28gF4FLK2DgC8Ae6krQuJ6CEi+hYRvWhYx0FEkwBeA5lJvC7jIKKvQTKlIoB/Xqdx/AmAP4cM040K/V6Xv7eW6f/NMiprOg7rngCAPyGi7xDRPxHRlrUehwdvAvAZYVHYtRyHEMIA8A4A34M07pcB+Lu1HgdkGPnFlpyTAPB6z3dWH2u5bFiPH3iWUQAuAfB1yGXkuwGctV5PA5i2/r4OsvbOuGdbfck1UY4Dkj1/BcBvDsH5yEDKNS9b63EAuAbAbX7bWuvzAWCH9XvM+u5b1+F8zECuqH7Ceu+3APzjOt8fjwG4bj2uC4AkJAnaA8noPwTg99fp/ngNgPsA3ANJSr7Qzznp92fNdrReP50MAOTS6/6A9+6ER2dFhEa+33EA+ChkBvG6nw/r9bcC+NBajwOSpZ0EcASyAF4DwJ1DcD5+Zp3OBwEoA4hZr58H4NF1vE+vBvD99bpPAdwA4BvK6y8GcPsQ3B9vB/D+fs9LPz/nnFxDVuQDEcUA/D4AjgqYJStKhIh2Q9bCf2qYxkFE74GsQveb6zUOIiqQVTbaWn6+CsATaz0OIcSHhRDbhRAXAHghpEH5wbUeBxElOGqDiJIAXg1ZwG9NxyGkBfkSgB+0NvFSSCa9puNQvvpmAJ8edP8DjOMEgMuIaNbaxMsAPL4O41C/swnArwD420HH0RPWckZZ6x/Im+wUAAOS7f08gN8A8H3r531wsn5/HFJvexjSqfkaZTvvt77fsn7/4VqPA9LJKiBv1Ietn19Yh3FsgYwweATSmP0vAIn1uC7K9i5Af9E1UZyPPOTS/RHr/f8JIL5O9+n5kE7BRyClil3rdV0gDdwl6/zc/jLk8/II5AQ4vU7j+DTkhPsYeoxEi+JHlzXQ0NDQGGGcc3KNhoaGxrkEbeQ1NDQ0RhjayGtoaGiMMLSR19DQ0BhhaCOvoaGhMcLQRl5DQ0NjhKGNvIaGhsYI4/8By0cGuqSi9oAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.statespace.tools import diff\n",
    "\n",
    "df['d1'] = diff(df['b'],k_diff=1)\n",
    "\n",
    "df['d1'].plot(title=\"FIRST DIFFERENCE DATA\", ylim=[-30, 30]).autoscale(axis='x',tight=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the MA for this series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['d1_ma'] = df['d1'].rolling(12).mean()\n",
    "\n",
    "df['d1_ma'].plot(title=\"MA OF FIRST DIFFERENCE DATA\", ylim=[-10, 10]).autoscale(axis='x',tight=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly differencing stablized the original series and now it seems like a stationary series."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lagging\n",
    "\n",
    "It is also known as `backshifting`, lagging notation reflects the value of `y` at a prior point in time. This is useful technique for performing regression.\n",
    "\n",
    "\\begin{split}L{y_t} = y_{t-1} & \\text{      one lag shifts the data back one period}\\\\\n",
    "L^{2}{y_t} = y_{t-2} & \\text{      two lags shift the data back two periods} \\end{split}\n",
    "\n",
    "<br>\n",
    "<table>\n",
    "<tr><td>{y_t}</td><td>6</td><td>8</td><td>3</td><td>4</td><td>9</td><td>2</td><td>5</td></tr>\n",
    "<tr><td>$y_{t-1}$</td><td>8</td><td>3</td><td>4</td><td>9</td><td>2</td><td>5</td></tr>\n",
    "<tr><td>$y_{t-2}$</td><td>3</td><td>4</td><td>9</td><td>2</td><td>5</td></tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autocorrelation function/Partial autocorrelation function\n",
    "\n",
    "We need to look at <em>covariance</em> and <em>correlation</em> as they relate to lagged (shifted) samples of a time series.\n",
    "\n",
    "**Contents:**\n",
    "\n",
    "* ACF and PACF charts\n",
    "* Create these charts for multiple times series, one with seasonality and another without\n",
    "* Calculate Orders PQD terms for ARIMA off charts\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Related Functions:</h4>\n",
    "<tt><strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.acovf.html'>stattools.acovf</a></strong><font color=green>(x[, unbiased, demean, fft, …])</font>&nbsp;Autocovariance for 1D<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.acf.html'>stattools.acf</a></strong><font color=green>(x[, unbiased, nlags, qstat, …])</font>&nbsp;&nbsp;Autocorrelation function for 1d arrays<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.pacf.html'>stattools.pacf</a></strong><font color=green>(x[, nlags, method, alpha])</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Partial autocorrelation estimated<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.pacf_yw.html'>stattools.pacf_yw</a></strong><font color=green>(x[, nlags, method])</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Partial autocorrelation estimated with non-recursive yule_walker<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.pacf_ols.html'>stattools.pacf_ols</a></strong><font color=green>(x[, nlags])</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Calculate partial autocorrelations</tt>\n",
    "   \n",
    "<h4>Related Plot Methods:</h4>\n",
    "<tt><strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.graphics.tsaplots.plot_acf.html'>tsaplots.plot_acf</a></strong><font color=green>(x)</font>&nbsp;&nbsp;&nbsp;Plot the autocorrelation function<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.graphics.tsaplots.plot_pacf.html'>tsaplots.plot_pacf</a></strong><font color=green>(x)</font>&nbsp;&nbsp;Plot the partial autocorrelation function</tt>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocovariance for 1D\n",
    "\n",
    "In a <em>deterministic</em> process, like $y=sin(x)$, we always know the value of $y$ for a given value of $x$. However, in a <em>stochastic</em> process there is always some randomness that prevents us from knowing the value of $y$. Instead, we analyze the past (or <em>lagged</em>) behavior of the system to derive a probabilistic estimate for $\\hat{y}$.\n",
    "\n",
    "One useful descriptor is <em>covariance</em>. When talking about dependent and independent $x$ and $y$ variables, covariance describes how the variance in $x$ relates to the variance in $y$. Here the size of the covariance isn't really important, as $x$ and $y$ may have very different scales. However, if the covariance is positive it means that $x$ and $y$ are changing in the same direction, and may be related.\n",
    "\n",
    "With a time series, $x$ is a fixed interval. Here we want to look at the variance of $y_t$ against lagged or shifted values of $y_{t+k}$\n",
    "\n",
    "\n",
    "For a stationary time series, the autocovariance function for $\\gamma$ (gamma) is given as:\n",
    "\n",
    "${\\displaystyle {\\gamma}_{XX}(t_{1},t_{2})=\\operatorname {Cov} \\left[X_{t_{1}},X_{t_{2}}\\right]=\\operatorname {E} [(X_{t_{1}}-\\mu _{t_{1}})(X_{t_{2}}-\\mu _{t_{2}})]}$\n",
    "\n",
    "We can calculate a specific $\\gamma_k$ with:\n",
    "\n",
    "${\\displaystyle \\gamma_k = \\frac 1 n \\sum\\limits_{t=1}^{n-k} (y_t - \\bar{y})(y_{t+k}-\\bar{y})}$\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'a':[1, 10, 7, 13, 6, 18, 9, 25, 13]})\n",
    "\n",
    "df.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 44.22222222,  -6.30864198,  19.60493827, -11.62962963,\n",
       "         7.80246914, -13.58024691,  -0.14814815, -15.9382716 ,\n",
       "        -1.91358025])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import acovf, acf\n",
    "arr = acovf(df['a'])\n",
    "\n",
    "arr"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unbiased autocovariance\n",
    "\n",
    "Note that the number of terms in the calculations above are decreasing. Statsmodels can return an \"unbiased\" autocovariance where instead of dividing by n instaed of $n-k$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 44.22222222,  -6.30864198,  19.60493827, -11.62962963,\n",
       "         7.80246914, -13.58024691,  -0.14814815, -15.9382716 ,\n",
       "        -1.91358025])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = acovf(df['a'])\n",
    "\n",
    "arr1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocorrelation for 1D\n",
    "\n",
    "The correlation $\\rho$ (rho) between two variables $y_1,y_2$ is given as:\n",
    "\n",
    "### $\\rho = \\frac {\\operatorname E[(y_1−\\mu_1)(y_2−\\mu_2)]} {\\sigma_{1}\\sigma_{2}} = \\frac {\\operatorname {Cov} (y_1,y_2)} {\\sigma_{1}\\sigma_{2}}$,\n",
    "\n",
    "where $E$ is the expectation operator, $\\mu_{1},\\sigma_{1}$ and $\\mu_{2},\\sigma_{2}$ are the means and standard deviations of $y_1$ and $y_2$.\n",
    "\n",
    "When working with a single variable (i.e. <em>autocorrelation</em>) we would consider $y_1$ to be the original series and $y_2$ a lagged version of it. Note that with autocorrelation we work with $\\bar y$, that is, the full population mean, and <em>not</em> the means of the reduced set of lagged factors (see note below).\n",
    "\n",
    "Thus, the formula for $\\rho_k$ for a time series at lag $k$ is:\n",
    "\n",
    "${\\displaystyle \\rho_k = \\frac {\\sum\\limits_{t=1}^{n-k} (y_t - \\bar{y})(y_{t+k}-\\bar{y})} {\\sum\\limits_{t=1}^{n} (y_t - \\bar{y})^2}}$\n",
    "\n",
    "This can be written in terms of the covariance constant $\\gamma_k$ as:\n",
    "\n",
    "${\\displaystyle \\rho_k = \\frac {\\gamma_k n} {\\gamma_0 n} = \\frac {\\gamma_k} {\\sigma^2}}$\n",
    "\n",
    "Note that ACF values are bound by -1 and 1. That is, ${\\displaystyle -1 \\leq \\rho_k \\leq 1}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        , -0.14265773,  0.44332775, -0.26298157,  0.17643774,\n",
       "       -0.30709101, -0.00335008, -0.36041318, -0.04327192])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = acf(df['a'])\n",
    "\n",
    "arr3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partial Autocorrelation\n",
    "\n",
    "Partial autocorrelations measure the linear dependence of one variable after removing the effect of other variable(s) that affect both variables. That is, the partial autocorrelation at lag $k$ is the autocorrelation between $y_t$ and $y_{t+k}$ that is not accounted for by lags $1$ through $k−1$.\n",
    "\n",
    "A common method employs the non-recursive <a href='https://en.wikipedia.org/wiki/Autoregressive_model#Calculation_of_the_AR_parameters'>Yule-Walker Equations</a>:\n",
    "\n",
    "$\\phi_0 = 1\\\\\n",
    "\\phi_1 = \\rho_1 = -0.50\\\\\n",
    "\\phi_2 = \\frac {\\rho_2 - {\\rho_1}^2} {1-{\\rho_1}^2} = \\frac {(-0.20) - {(-0.50)}^2} {1-{(-0.50)}^2}= \\frac {-0.45} {0.75} = -0.60$\n",
    "\n",
    "As $k$ increases, we can solve for $\\phi_k$ using matrix algebra and the <a href='https://en.wikipedia.org/wiki/Levinson_recursion'>Levinson–Durbin recursion</a> algorithm which maps the sample autocorrelations $\\rho$ to a <a href='https://en.wikipedia.org/wiki/Toeplitz_matrix'>Toeplitz</a> diagonal-constant matrix."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\displaystyle \\begin{pmatrix}\\rho_0&\\rho_1&\\cdots &\\rho_{k-1}\\\\\n",
    "\\rho_1&\\rho_0&\\cdots &\\rho_{k-2}\\\\\n",
    "\\vdots &\\vdots &\\ddots &\\vdots \\\\\n",
    "\\rho_{k-1}&\\rho_{k-2}&\\cdots &\\rho_0\\\\\n",
    "\\end{pmatrix}\\quad \\begin{pmatrix}\\phi_{k1}\\\\\\phi_{k2}\\\\\\vdots\\\\\\phi_{kk}\\end{pmatrix}\n",
    "\\mathbf = \\begin{pmatrix}\\rho_1\\\\\\rho_2\\\\\\vdots\\\\\\rho_k\\end{pmatrix}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        , -0.14265773,  0.43176344, -0.20758442, -0.04572862])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import pacf_yw\n",
    "arr4 = pacf_yw(df['a'],nlags=4,method='mle')\n",
    "arr4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><strong>NOTE:</strong> We passed in <tt><font color=green>method='mle'</font></tt> above in order to use biased ACF coefficients. \"mle\" stands for \"maximum likelihood estimation\". Alternatively we can pass <tt>method='unbiased'</tt> (the statsmodels default):</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        , -0.16048995,  0.5586243 , -0.39456104,  0.01906252])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4 = pacf_yw(df['a'],nlags=4,method='adjusted')\n",
    "arr4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Partial autocrrelation with OLS\n",
    "\n",
    "This provides partial autocorrelations with <a href='https://en.wikipedia.org/wiki/Ordinary_least_squares'>ordinary least squares</a> (OLS) estimates for each lag instead of Yule-Walker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        , -0.13833492,  1.13495418, -0.04476691,  0.5979815 ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import pacf_ols\n",
    "arr5 = pacf_ols(df['a'],nlags=4)\n",
    "arr5"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "The arrays returned by `.acf(df)` and `.pacf_yw(df)` show the magnitude of the autocorrelation for a given $y$ at time $t$.\n",
    "\n",
    "Pandas has a built-in plotting function that plots increasing $y_t$ values on the horizontal axis against lagged versions of the values $y_{t+1}$ on the vertical axis. If a dataset is non-stationary with an upward trend, then neighboring values should trend in the same way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import lag_plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Office Supplies    6026\n",
       "Furniture          2121\n",
       "Technology         1847\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>16.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>288.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>19.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>685.340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales\n",
       "Order Date         \n",
       "2014-01-03   16.448\n",
       "2014-01-04  288.060\n",
       "2014-01-05   19.536\n",
       "2014-01-06  685.340"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the sales data of Office Supplies\n",
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df = df[df['Category']=='Office Supplies']\n",
    "df = df.groupby(by='Order Date').agg({'Sales':sum})\n",
    "df.sort_index(inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>324.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12</th>\n",
       "      <td>708.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-19</th>\n",
       "      <td>2337.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-26</th>\n",
       "      <td>1143.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-02</th>\n",
       "      <td>368.784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-05   324.044\n",
       "2014-01-12   708.004\n",
       "2014-01-19  2337.764\n",
       "2014-01-26  1143.170\n",
       "2014-02-02   368.784"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.resample('W').sum()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='y(t)', ylabel='y(t + 1)'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lag_plot(df['Sales'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does not show strong relationship since the time series is nearly stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ACF Plots\n",
    "\n",
    "Plotting the magnitude of the autocorrelations over the first few (20-40) lags can say a lot about time series.\n",
    "\n",
    "Example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.00000000e+00,  3.12440082e-01,  2.49173025e-01,  1.39336867e-01,\n",
       "        1.84489786e-01,  1.67531418e-01,  1.11379734e-01,  1.84930768e-01,\n",
       "        8.24198806e-02,  8.74684994e-02,  4.89924777e-02,  6.22098365e-02,\n",
       "        1.65448013e-01,  1.49072360e-01,  7.97573259e-02,  7.65911202e-02,\n",
       "        7.55594141e-02, -7.46386648e-04,  4.97054222e-02, -2.10402529e-02,\n",
       "       -2.49880823e-02, -6.68093251e-02, -1.79718855e-02, -3.42941758e-02])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acf(df['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Autocorrelation: Weekly Sales of Office Supplies'\n",
    "lags = 40\n",
    "plot_acf(df['Sales'], title=title)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a typical ACF plot for stationary data, with lags on the horizontal axis and correlations on the vertical axis. The first value $y_0$ is always 1. A sharp dropoff indicates that there is no AR component in the ARIMA model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**With non stationary data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PACF Plots\n",
    "\n",
    "It works best with stationary data. Let's look at the sales data again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Partial Autocorrelation: Weekly Sales of Office Supplies'\n",
    "lags = 40\n",
    "plot_pacf(df['Sales'], title=title)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**With non-stationary data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\"><strong>A NOTE ABOUT AUTOCORRELATION:</strong> We can use lagged correlations using the Pearson Correlation Coefficient given by:<br><br>\n",
    "\n",
    "${\\displaystyle r_{xy}={\\frac {\\sum _{i=1}^{n}(x_{i}-{\\bar {x}})(y_{i}-{\\bar {y}})}{{\\sqrt {\\sum _{i=1}^{n}(x_{i}-{\\bar {x}})^{2}}}{\\sqrt {\\sum _{i=1}^{n}(y_{i}-{\\bar {y}})^{2}}}}}}$\n",
    "\n",
    "These are easily calculated in numpy with <tt>numpy.corrcoef(x,y)</tt> and in Excel with <tt>=CORREL(x,y)</tt>.<br><br>\n",
    "Using our example, $r_0$ is still $1$, but to solve for $r_1$:\n",
    "\n",
    "However, there are some shortcomings. Using the Pearson method, the second-to-last term $r_{k-1}$ will always be $1$ and the last term $r_k$ will always be undefined.\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoregressive Model\n",
    "\n",
    "In moving average model like Holt-Winters, we forecast the variable of interest using a linear combination of predictors. \n",
    "\n",
    "In an autoregressive model, we forecast using a linear combinations of past values of the variable. The term autoregressive describes a regression of the variable against itself. An autoregression is run against a set of lagged values of order $p$.\n",
    "\n",
    "### $y_{t} = c + \\phi_{1}y_{t-1} + \\phi_{2}y_{t-2} + \\dots + \\phi_{p}y_{t-p} + \\varepsilon_{t}$\n",
    "\n",
    "where $c$ is a constant, $\\phi_{1}$ and $\\phi_{2}$ are lag coefficients up to order $p$, and $\\varepsilon_{t}$ is white noise.\n",
    "\n",
    "For example, an <strong>AR(1)</strong> model would follow the formula\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "whereas an <strong>AR(2)</strong> model would follow the formula\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\phi_{2}y_{t-2} + \\varepsilon_{t}$\n",
    "\n",
    "and so on.\n",
    "\n",
    "Note that the lag coeffients are usually less than one, as we usually restrict autoregressive models to stationary data.<br>\n",
    "Specifically, for an <strong>AR(1)</strong> model: $-1 \\lt \\phi_1 \\lt 1$<br>\n",
    "and for an <strong>AR(2)</strong> model: $-1 \\lt \\phi_2 \\lt 1, \\ \\phi_1 + \\phi_2 \\lt 1, \\ \\phi_2 - \\phi_1 \\lt 1$<br>\n",
    "\n",
    "Models <strong>AR(3)</strong> and higher become mathematically very complex. Fortunately statsmodels does all the heavy lifting for us.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><h3>Related Functions:</h3>\n",
    "<tt><strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.ar_model.AR.html'>ar_model.AR</a></strong><font color=green>(endog[, dates, freq, missing])</font>&nbsp;&nbsp;Autoregressive AR(p) model<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.ar_model.ARResults.html'>ar_model.ARResults</a></strong><font color=green>(model, params[, …])</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Class to hold results from fitting an AR model</tt>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the Furniture sales data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load specific forecasting tools\n",
    "from statsmodels.tsa.ar_model import AR,ARResults\n",
    "\n",
    "#plot the sales data of Office Supplies\n",
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df = df[df['Category']=='Furniture']\n",
    "df = df.groupby(by='Order Date').agg({'Sales':sum})\n",
    "df.sort_index(inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>5618.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
       "      <td>2463.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>13448.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>8439.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>5745.482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Order Date           \n",
       "2014-01-31   5618.859\n",
       "2014-02-28   2463.324\n",
       "2014-03-31  13448.468\n",
       "2014-04-30   8439.321\n",
       "2014-05-31   5745.482"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.resample('M').sum()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Order Date'), Text(0, 0.5, 'Sales Data')]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title='Monthly Furniture Sales Data'\n",
    "ylabel='Sales Data'\n",
    "xlabel='Order Date'\n",
    "\n",
    "ax = df['Sales'].plot(figsize=(12,5),title=title)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into train/test sets\n",
    "The goal in this section is to:\n",
    "* Split known data into a training set of records on which to fit the model\n",
    "* Use the remaining records for testing, to evaluate the model\n",
    "* Fit the model again on the <em>full</em> set of records\n",
    "* Predict a future set of values using the model\n",
    "\n",
    "As a general rule you should set the length of your test set equal to your intended forecast size. That is, for a monthly dataset you might want to forecast out one more year. Therefore your test set should be one year long.\n",
    "\n",
    "<div class=\"alert alert-info\"><strong>NOTE: </strong>For many training and testing applications we would use the <tt>train_test_split()</tt> function available from Python's <a href='https://scikit-learn.org/stable/'>scikit-learn</a> library. This won't work here as <tt>train_test_split()</tt> takes <em>random samples</em> of data from the population.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is on week level and Suppose I want to forecast for next 1 year so in this case the test set size should be equal to 52 weeks or 52 points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size is 42\n",
      "Test size is 6\n"
     ]
    }
   ],
   "source": [
    "train = df.iloc[:len(df)-6]\n",
    "test = df.iloc[len(df)-6:]\n",
    "\n",
    "print(f\"Train size is {len(train)}\")\n",
    "print(f\"Test size is {len(test)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model's fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from statsmodels.tsa.ar_model import AutoReg"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit an autoregressive model with order(p) = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            AutoReg Model Results                             \n",
      "==============================================================================\n",
      "Dep. Variable:                  Sales   No. Observations:                   42\n",
      "Model:                     AutoReg(1)   Log Likelihood                -425.369\n",
      "Method:               Conditional MLE   S.D. of innovations           7753.664\n",
      "Date:                Sat, 26 Aug 2023   AIC                            856.738\n",
      "Time:                        17:04:48   BIC                            861.879\n",
      "Sample:                    02-28-2014   HQIC                           858.610\n",
      "                         - 06-30-2017                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1.128e+04   2434.613      4.631      0.000    6503.709     1.6e+04\n",
      "Sales.L1       0.2133      0.150      1.419      0.156      -0.081       0.508\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            4.6872           +0.0000j            4.6872            0.0000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mod1 = AutoReg(train['Sales'], 1, old_names=False)\n",
    "res1 = mod1.fit()\n",
    "print(res1.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag: [0.]\n",
      "Coefficients:\n",
      "const       11275.463768\n",
      "Sales.L1        0.213349\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(f'Lag: {res1.arfreq}')\n",
    "print(f'Coefficients:\\n{res1.params}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the general format for obtaining predictions\n",
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions1 = res1.predict(start=start, end=end, dynamic=False).rename('AR(1) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-07-31    14463.662359\n",
       "2017-08-31    14361.273027\n",
       "2017-09-30    14339.428356\n",
       "2017-10-31    14334.767815\n",
       "2017-11-30    14333.773493\n",
       "2017-12-31    14333.561355\n",
       "Freq: M, Name: AR(1) Predictions, dtype: float64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=14463.66236, expected=16380.9642\n",
      "predicted=14361.27303, expected=13623.496\n",
      "predicted=14339.42836, expected=29053.728000000003\n",
      "predicted=14334.76781, expected=22412.126200000002\n",
      "predicted=14333.77349, expected=29011.936999999998\n",
      "predicted=14333.56135, expected=40754.002799999995\n"
     ]
    }
   ],
   "source": [
    "#Comparing predictions to expected values\n",
    "for i in range(len(predictions1)):\n",
    "    print(f\"predicted={predictions1[i]:<11.10}, expected={test['Sales'][i]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Sales'].plot(legend=True)\n",
    "predictions1.plot(legend=True,figsize=(12,6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting an AR(6) model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            AutoReg Model Results                             \n",
      "==============================================================================\n",
      "Dep. Variable:                  Sales   No. Observations:                   42\n",
      "Model:                     AutoReg(6)   Log Likelihood                -372.453\n",
      "Method:               Conditional MLE   S.D. of innovations           7532.426\n",
      "Date:                Sat, 26 Aug 2023   AIC                            760.906\n",
      "Time:                        17:05:18   BIC                            773.574\n",
      "Sample:                    07-31-2014   HQIC                           765.327\n",
      "                         - 06-30-2017                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1.892e+04   5655.402      3.345      0.001    7833.787       3e+04\n",
      "Sales.L1       0.1358      0.166      0.815      0.415      -0.191       0.462\n",
      "Sales.L2      -0.0391      0.164     -0.238      0.812      -0.361       0.283\n",
      "Sales.L3       0.0134      0.159      0.084      0.933      -0.298       0.324\n",
      "Sales.L4      -0.2797      0.159     -1.759      0.079      -0.591       0.032\n",
      "Sales.L5      -0.0576      0.163     -0.354      0.723      -0.376       0.261\n",
      "Sales.L6      -0.0416      0.158     -0.263      0.793      -0.352       0.269\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            0.9662           -0.7960j            1.2519           -0.1097\n",
      "AR.2            0.9662           +0.7960j            1.2519            0.1097\n",
      "AR.3           -1.0439           -1.0925j            1.5111           -0.3714\n",
      "AR.4           -1.0439           +1.0925j            1.5111            0.3714\n",
      "AR.5           -0.6147           -2.5185j            2.5924           -0.2881\n",
      "AR.6           -0.6147           +2.5185j            2.5924            0.2881\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mod6 = AutoReg(train['Sales'], 6)\n",
    "res6 = mod6.fit()\n",
    "print(res6.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the general format for obtaining predictions\n",
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions6 = res6.predict(start=start, end=end, dynamic=False).rename('AR(6) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-07-31    17353.722450\n",
       "2017-08-31    16861.641613\n",
       "2017-09-30    15121.035234\n",
       "2017-10-31    14935.259943\n",
       "2017-11-30    14183.753740\n",
       "2017-12-31    14125.827519\n",
       "Freq: M, Name: AR(6) Predictions, dtype: float64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Sales'].plot(legend=True)\n",
    "predictions6.plot(legend=True,figsize=(12,6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of choosing the order(p), we can let `statsmodel` to find out best p value for us."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit an AR(p) model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.ar_model import ar_select_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = ar_select_order(train['Sales'], maxlag=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.ar_lags"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model with p as 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            AutoReg Model Results                             \n",
      "==============================================================================\n",
      "Dep. Variable:                  Sales   No. Observations:                   42\n",
      "Model:                    AutoReg(12)   Log Likelihood                -287.174\n",
      "Method:               Conditional MLE   S.D. of innovations           3475.564\n",
      "Date:                Sat, 26 Aug 2023   AIC                            602.347\n",
      "Time:                        17:05:47   BIC                            621.964\n",
      "Sample:                    01-31-2015   HQIC                           608.623\n",
      "                         - 06-30-2017                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       8305.2257   8699.855      0.955      0.340   -8746.177    2.54e+04\n",
      "Sales.L1       0.0125      0.091      0.137      0.891      -0.166       0.191\n",
      "Sales.L2       0.0337      0.093      0.365      0.715      -0.148       0.215\n",
      "Sales.L3      -0.1113      0.089     -1.251      0.211      -0.286       0.063\n",
      "Sales.L4      -0.1412      0.091     -1.560      0.119      -0.319       0.036\n",
      "Sales.L5       0.0110      0.090      0.122      0.903      -0.166       0.188\n",
      "Sales.L6      -0.0750      0.088     -0.849      0.396      -0.248       0.098\n",
      "Sales.L7      -0.0189      0.100     -0.189      0.850      -0.215       0.177\n",
      "Sales.L8       0.0520      0.107      0.485      0.628      -0.158       0.262\n",
      "Sales.L9      -0.0544      0.103     -0.529      0.597      -0.256       0.147\n",
      "Sales.L10     -0.1108      0.107     -1.036      0.300      -0.320       0.099\n",
      "Sales.L11     -0.0141      0.102     -0.139      0.890      -0.214       0.186\n",
      "Sales.L12      0.9570      0.100      9.558      0.000       0.761       1.153\n",
      "                                    Roots                                     \n",
      "==============================================================================\n",
      "                   Real          Imaginary           Modulus         Frequency\n",
      "------------------------------------------------------------------------------\n",
      "AR.1            -1.0095           -0.0000j            1.0095           -0.5000\n",
      "AR.2            -0.8629           -0.4996j            0.9971           -0.4165\n",
      "AR.3            -0.8629           +0.4996j            0.9971            0.4165\n",
      "AR.4            -0.5296           -0.8673j            1.0161           -0.3372\n",
      "AR.5            -0.5296           +0.8673j            1.0161            0.3372\n",
      "AR.6             0.0090           -0.9987j            0.9988           -0.2486\n",
      "AR.7             0.0090           +0.9987j            0.9988            0.2486\n",
      "AR.8             0.5156           -0.8439j            0.9889           -0.1627\n",
      "AR.9             0.5156           +0.8439j            0.9889            0.1627\n",
      "AR.10            0.8598           -0.5040j            0.9967           -0.0844\n",
      "AR.11            0.8598           +0.5040j            0.9967            0.0844\n",
      "AR.12            1.0405           -0.0000j            1.0405           -0.0000\n",
      "------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mod12 = AutoReg(train['Sales'], 12)\n",
    "res12 = mod12.fit()\n",
    "print(res12.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the general format for obtaining predictions\n",
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions12 = res12.predict(start=start, end=end, dynamic=False).rename('AR(6) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Sales'].plot(legend=True)\n",
    "predictions12.plot(legend=True,figsize=(12,6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This seems far better than earlier models with order 6 and 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model's Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AR(1) Error: 199910484.0\n",
      "AR(6) Error: 198398340.0\n",
      "AR(12) Error: 32106243.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "labels = ['AR(1)','AR(6)','AR(12)']\n",
    "preds = [predictions1, predictions6, predictions12]  # these are variables, not strings!\n",
    "\n",
    "for i in range(3):\n",
    "    error = mean_squared_error(test['Sales'], preds[i])\n",
    "    print(f'{labels[i]} Error: {error:11.10}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly model AR(12) outperform the other models with lower order.\n",
    "\n",
    "Another method is the <a href='https://en.wikipedia.org/wiki/Akaike_information_criterion'>Akaike information criterion</a> (AIC), which does a better job of evaluating models by avoiding overfitting.<br>\n",
    "Fortunately this is available directly from the fit model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AR(1) AIC: 856.74\n",
      "AR(6) AIC: 760.91\n",
      "AR(12) AIC: 602.35\n"
     ]
    }
   ],
   "source": [
    "modls = [res1,res6,res12]\n",
    "\n",
    "for i in range(3):\n",
    "    print(f'{labels[i]} AIC: {modls[i].aic:6.5}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting in future\n",
    "Now we are ready to train our model whole data and predict thje future sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            AutoReg Model Results                             \n",
      "==============================================================================\n",
      "Dep. Variable:                  Sales   No. Observations:                   48\n",
      "Model:                    AutoReg(12)   Log Likelihood                -346.733\n",
      "Method:               Conditional MLE   S.D. of innovations           3686.905\n",
      "Date:                Sat, 26 Aug 2023   AIC                            721.467\n",
      "Time:                        17:14:19   BIC                            743.636\n",
      "Sample:                    01-31-2015   HQIC                           729.204\n",
      "                         - 12-31-2017                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       3301.0384   7199.471      0.459      0.647   -1.08e+04    1.74e+04\n",
      "Sales.L1       0.0888      0.086      1.033      0.302      -0.080       0.257\n",
      "Sales.L2       0.0156      0.089      0.175      0.861      -0.159       0.190\n",
      "Sales.L3      -0.0406      0.086     -0.472      0.637      -0.209       0.128\n",
      "Sales.L4      -0.1389      0.091     -1.534      0.125      -0.316       0.039\n",
      "Sales.L5       0.0694      0.090      0.768      0.443      -0.108       0.247\n",
      "Sales.L6      -0.0513      0.090     -0.571      0.568      -0.228       0.125\n",
      "Sales.L7      -0.0302      0.090     -0.337      0.736      -0.206       0.146\n",
      "Sales.L8       0.0268      0.089      0.301      0.764      -0.148       0.201\n",
      "Sales.L9      -0.0128      0.088     -0.145      0.885      -0.186       0.160\n",
      "Sales.L10      0.0279      0.088      0.317      0.751      -0.145       0.200\n",
      "Sales.L11     -0.0140      0.084     -0.167      0.867      -0.178       0.150\n",
      "Sales.L12      0.9549      0.081     11.802      0.000       0.796       1.113\n",
      "                                    Roots                                     \n",
      "==============================================================================\n",
      "                   Real          Imaginary           Modulus         Frequency\n",
      "------------------------------------------------------------------------------\n",
      "AR.1             1.0091           -0.0000j            1.0091           -0.0000\n",
      "AR.2             0.8536           -0.5011j            0.9898           -0.0845\n",
      "AR.3             0.8536           +0.5011j            0.9898            0.0845\n",
      "AR.4             0.5088           -0.8567j            0.9964           -0.1647\n",
      "AR.5             0.5088           +0.8567j            0.9964            0.1647\n",
      "AR.6            -1.0180           -0.0000j            1.0180           -0.5000\n",
      "AR.7            -0.8526           -0.5142j            0.9957           -0.4136\n",
      "AR.8            -0.8526           +0.5142j            0.9957            0.4136\n",
      "AR.9             0.0188           -1.0132j            1.0134           -0.2470\n",
      "AR.10            0.0188           +1.0132j            1.0134            0.2470\n",
      "AR.11           -0.5168           -0.8732j            1.0147           -0.3351\n",
      "AR.12           -0.5168           +0.8732j            1.0147            0.3351\n",
      "------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#fit the model over whole data\n",
    "mod12 = AutoReg(df['Sales'], 12)\n",
    "res12 = mod12.fit()\n",
    "print(res12.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-31    10539.256282\n",
       "2018-02-28     8971.701857\n",
       "2018-03-31     6709.259836\n",
       "2018-04-30     9394.932797\n",
       "2018-05-31    19602.224067\n",
       "2018-06-30    16080.154011\n",
       "2018-07-31    19322.470654\n",
       "2018-08-31    16801.009220\n",
       "2018-09-30    29752.897947\n",
       "2018-10-31    26098.642297\n",
       "2018-11-30    30031.975497\n",
       "2018-12-31    41938.762520\n",
       "2019-01-31    12518.082274\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = len(df)\n",
    "end = len(df)+12\n",
    "\n",
    "pred_future = res12.predict(start=start, end=end, dynamic=False)\n",
    "\n",
    "pred_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sales'].plot()\n",
    "pred_future.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoregressive Moving Average(ARMA) Model\n",
    "\n",
    "It's a class of time series models used for analyzing and forecasting time-dependent data. ARMA models are used to describe and capture the patterns and dependencies present in a time series dataset. They combine autoregressive (AR) and moving average (MA) components.\n",
    "\n",
    "1. **AutoRegressive (AR) Component**: The AR part of an ARMA model involves modeling the current value of the series as a linear combination of its previous values (lags). An AR(p) model considers the relationship between the current value and the p previous values. The equation for an AR(p) model is:\n",
    "\n",
    "   $y_t = c + \\phi_1 y_{t-1} + \\phi_2 y_{t-2} + \\ldots + \\phi_p y_{t-p} + \\varepsilon_t$\n",
    "\n",
    "   where $y_t$ is the current value of the time series at time $t$, $c$ is a constant, $\\phi_1$, $\\phi_2$, $\\ldots$, $\\phi_p$ are the autoregressive coefficients, $y_{t-1}$, $y_{t-2}$, $\\ldots$, $y_{t-p}$ are the lagged values, and $\\varepsilon_t$ is the white noise error term.\n",
    "\n",
    "2. **Moving Average (MA) Component**: The MA part of an ARMA model involves modeling the current value of the series as a linear combination of the current and past white noise error terms. An MA(q) model considers the relationship between the current value and the q previous error terms. The equation for an MA(q) model is:\n",
    "\n",
    "    $y_t = c + \\theta_1 \\varepsilon_{t-1} + \\theta_2 \\varepsilon_{t-2} + \\ldots + \\theta_q \\varepsilon_{t-q} + \\varepsilon_t$\n",
    "\n",
    "   where $y_t$, $c$, $theta_1$, $theta_2$, $\\ldots$, $theta_q$, $\\varepsilon_{t-1}$, $\\varepsilon_{t-2}$, $\\ldots$, $\\varepsilon_{t-q}$, and $\\varepsilon_t$ are similar to the AR model.\n",
    "\n",
    "Combining both the AR and MA components, an ARMA(p, q) model is defined as:\n",
    "\n",
    "$y_t = c + \\phi_1 y_{t-1} + \\phi_2 y_{t-2} + \\ldots + \\phi_p y_{t-p} + \\theta_1 \\varepsilon_{t-1} + \\theta_2 \\varepsilon_{t-2} + \\ldots + \\theta_q \\varepsilon_{t-q} + \\varepsilon_t$\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*ARMA(p=1, q=1) = MA(q=1) + AR(p=1)*\n",
    "\n",
    "So if AR(1) follow the formula:\n",
    "\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "and if MA(1) follow the formula:\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = \\mu + \\theta_{1}\\varepsilon_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "Then ARMA(1, 1) can be given by:\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\theta_{1}\\varepsilon_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "\n",
    "**Note**\n",
    "\n",
    "- ARMA models can be used on stationary datasets.\n",
    "- For non-stationary datasets with a trend component, ARIMA models apply a differencing coefficient as well.\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Related Functions:</h4>\n",
    "<tt><strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima_model.ARMA.html'>arima_model.ARMA</a></strong><font color=green>(endog, order[, exog, …])</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Autoregressive Moving Average ARMA(p,q) model<br>\n",
    "<strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.arima_model.ARMAResults.html'>arima_model.ARMAResults</a></strong><font color=green>(model, params[, …])</font>&nbsp;&nbsp;&nbsp;Class to hold results from fitting an ARMA model<br>\n",
    "<strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.html'>kalmanf.kalmanfilter.KalmanFilter</a></strong>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Kalman Filter code intended for use with the ARMA model</tt>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pmdarima\n",
      "  Downloading pmdarima-2.0.3-cp39-cp39-macosx_10_9_x86_64.whl (608 kB)\n",
      "\u001b[K     |████████████████████████████████| 608 kB 3.6 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.21.2 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (1.24.3)\n",
      "Requirement already satisfied: urllib3 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (1.26.9)\n",
      "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (61.2.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (1.1.0)\n",
      "Requirement already satisfied: scikit-learn>=0.22 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (1.0.2)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (1.7.3)\n",
      "Requirement already satisfied: statsmodels>=0.13.2 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (0.13.2)\n",
      "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (0.29.28)\n",
      "Requirement already satisfied: pandas>=0.19 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pmdarima) (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.19->pmdarima) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas>=0.19->pmdarima) (1.16.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from scikit-learn>=0.22->pmdarima) (2.2.0)\n",
      "Collecting numpy>=1.21.2\n",
      "  Downloading numpy-1.22.4-cp39-cp39-macosx_10_15_x86_64.whl (17.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 17.7 MB 3.9 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: patsy>=0.5.2 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from statsmodels>=0.13.2->pmdarima) (0.5.2)\n",
      "Requirement already satisfied: packaging>=21.3 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from statsmodels>=0.13.2->pmdarima) (21.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/ajitkumarsingh/opt/anaconda3/lib/python3.9/site-packages (from packaging>=21.3->statsmodels>=0.13.2->pmdarima) (3.0.4)\n",
      "Installing collected packages: numpy, pmdarima\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.24.3\n",
      "    Uninstalling numpy-1.24.3:\n",
      "      Successfully uninstalled numpy-1.24.3\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "daal4py 2021.5.0 requires daal==2021.4.0, which is not installed.\n",
      "numba 0.55.1 requires numpy<1.22,>=1.18, but you have numpy 1.22.4 which is incompatible.\u001b[0m\n",
      "Successfully installed numpy-1.22.4 pmdarima-2.0.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pmdarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# Load specific forecasting tools\n",
    "from statsmodels.tsa.arima_model import ARMA,ARMAResults,ARIMA,ARIMAResults\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf # for determining (p,q) orders\n",
    "from pmdarima import auto_arima # for determining ARIMA orders\n",
    "\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the sales data of Office Supplies\n",
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df = df[df['Category']=='Furniture']\n",
    "df = df.groupby(by='Order Date').agg({'Sales':sum})\n",
    "df.sort_index(inplace=True)\n",
    "df.head(4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.resample('MS').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>6242.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>1839.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>14573.956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>7944.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>6912.787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Order Date           \n",
       "2014-01-01   6242.525\n",
       "2014-02-01   1839.658\n",
       "2014-03-01  14573.956\n",
       "2014-04-01   7944.837\n",
       "2014-05-01   6912.787"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automate the augmented Dickey-Fuller Test\n",
    "\n",
    "To check if the series is stationary let's write a function that perform the augmented Dickey-Fuller Test.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the original data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sales'].plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run the augmented dickey-fuller test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic      -4.699026\n",
      "p-value                  0.000085\n",
      "# lags used              0.000000\n",
      "# observations          47.000000\n",
      "critical value (1%)     -3.577848\n",
      "critical value (5%)     -2.925338\n",
      "critical value (10%)    -2.600774\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df['Sales'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine the (p,q) ARMA orders\n",
    "\n",
    "I will be using <tt>pmdarima.auto_arima</tt> to get $p$ and $q$ value recommendations for the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>48</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)</td> <th>  Log Likelihood     </th> <td>-502.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 26 Aug 2023</td> <th>  AIC                </th> <td>1011.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>18:43:58</td>     <th>  BIC                </th> <td>1017.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-2014</td>    <th>  HQIC               </th> <td>1013.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-2017</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td> 1.084e+04</td> <td> 2695.066</td> <td>    4.021</td> <td> 0.000</td> <td> 5554.237</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.3056</td> <td>    0.131</td> <td>    2.328</td> <td> 0.020</td> <td>    0.048</td> <td>    0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 7.318e+07</td> <td>    0.160</td> <td> 4.56e+08</td> <td> 0.000</td> <td> 7.32e+07</td> <td> 7.32e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>3.70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th> <td>0.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.88</td> <th>  Skew:              </th> <td>0.64</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.22</td> <th>  Kurtosis:          </th> <td>2.54</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 2.09e+24. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   48\n",
       "Model:               SARIMAX(1, 0, 0)   Log Likelihood                -502.820\n",
       "Date:                Sat, 26 Aug 2023   AIC                           1011.640\n",
       "Time:                        18:43:58   BIC                           1017.253\n",
       "Sample:                    01-01-2014   HQIC                          1013.761\n",
       "                         - 12-01-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   1.084e+04   2695.066      4.021      0.000    5554.237    1.61e+04\n",
       "ar.L1          0.3056      0.131      2.328      0.020       0.048       0.563\n",
       "sigma2      7.318e+07      0.160   4.56e+08      0.000    7.32e+07    7.32e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 3.70\n",
       "Prob(Q):                              0.98   Prob(JB):                         0.16\n",
       "Heteroskedasticity (H):               1.88   Skew:                             0.64\n",
       "Prob(H) (two-sided):                  0.22   Kurtosis:                         2.54\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 2.09e+24. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df['Sales'], seasonal=True).summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, 6)"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = df.iloc[:len(df)-6]\n",
    "test = df.iloc[len(df)-6:]\n",
    "\n",
    "len(train), len(test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit an ARMA(p,q) model\n",
    "\n",
    "If you want you can run <tt>help(ARMA)</tt> to learn what incoming arguments are available/expected, and what's being returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class ARMA in module statsmodels.tsa.arima_model:\n",
      "\n",
      "class ARMA(builtins.object)\n",
      " |  ARMA(*args, **kwargs)\n",
      " |  \n",
      " |  ARMA has been deprecated in favor of the new implementation\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  statsmodels.tsa.arima.model.ARIMA\n",
      " |      ARIMA models with a variety of parameter estimators\n",
      " |  statsmodels.tsa.statespace.SARIMAX\n",
      " |      SARIMAX models estimated using MLE\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ARMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Sales</td>      <th>  No. Observations:  </th>    <td>42</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(2, 0, 2)</td>  <th>  Log Likelihood     </th> <td>-436.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 26 Aug 2023</td> <th>  AIC                </th>  <td>885.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:27:54</td>     <th>  BIC                </th>  <td>896.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-2014</td>    <th>  HQIC               </th>  <td>889.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 06-01-2017</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td> 1.418e+04</td> <td> 1427.017</td> <td>    9.934</td> <td> 0.000</td> <td> 1.14e+04</td> <td>  1.7e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.6732</td> <td>    0.810</td> <td>    0.831</td> <td> 0.406</td> <td>   -0.914</td> <td>    2.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.1773</td> <td>    0.842</td> <td>    0.211</td> <td> 0.833</td> <td>   -1.474</td> <td>    1.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.5278</td> <td>    0.739</td> <td>   -0.714</td> <td> 0.475</td> <td>   -1.977</td> <td>    0.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.4528</td> <td>    0.771</td> <td>   -0.587</td> <td> 0.557</td> <td>   -1.964</td> <td>    1.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>  7.04e+07</td> <td>    0.014</td> <td> 4.87e+09</td> <td> 0.000</td> <td> 7.04e+07</td> <td> 7.04e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>8.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.93</td> <th>  Prob(JB):          </th> <td>0.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.31</td> <th>  Skew:              </th> <td>1.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.62</td> <th>  Kurtosis:          </th> <td>3.27</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 4.06e+25. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   No. Observations:                   42\n",
       "Model:                 ARIMA(2, 0, 2)   Log Likelihood                -436.980\n",
       "Date:                Sat, 26 Aug 2023   AIC                            885.960\n",
       "Time:                        19:27:54   BIC                            896.386\n",
       "Sample:                    01-01-2014   HQIC                           889.782\n",
       "                         - 06-01-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1.418e+04   1427.017      9.934      0.000    1.14e+04     1.7e+04\n",
       "ar.L1          0.6732      0.810      0.831      0.406      -0.914       2.260\n",
       "ar.L2          0.1773      0.842      0.211      0.833      -1.474       1.828\n",
       "ma.L1         -0.5278      0.739     -0.714      0.475      -1.977       0.921\n",
       "ma.L2         -0.4528      0.771     -0.587      0.557      -1.964       1.059\n",
       "sigma2       7.04e+07      0.014   4.87e+09      0.000    7.04e+07    7.04e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                 8.53\n",
       "Prob(Q):                              0.93   Prob(JB):                         0.01\n",
       "Heteroskedasticity (H):               1.31   Skew:                             1.10\n",
       "Prob(H) (two-sided):                  0.62   Kurtosis:                         3.27\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 4.06e+25. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(train['Sales'],order=(2,0,2))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions = results.predict(start=start, end=end).rename('ARMA(2,2) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-07-01    13115.074274\n",
       "2017-08-01    12031.112109\n",
       "2017-09-01    12543.910511\n",
       "2017-10-01    12696.894189\n",
       "2017-11-01    12890.811965\n",
       "2017-12-01    13048.480047\n",
       "Freq: MS, Name: ARMA(2,2) Predictions, dtype: float64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot predictions against known values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Order Date'), Text(0, 0.5, 'Sales Data')]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Furniture Sales Data'\n",
    "ylabel='Sales Data'\n",
    "xlabel='Order Date' # we don't really need a label here\n",
    "\n",
    "ax = test['Sales'].plot(legend=True,figsize=(12,6),title=title)\n",
    "predictions.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoregressive Integreted Moving Average(ARIMA)\n",
    "\n",
    "It's a more comprehensive and flexible time series modeling approach compared to the ARMA model. ARIMA models are used for analyzing, forecasting, and understanding time series data that might exhibit trends, seasonality, and other complex patterns.\n",
    "\n",
    "ARIMA models are built on three main components:\n",
    "\n",
    "1. **AutoRegressive (AR) Component**: Similar to the AR component in ARMA models, this part models the relationship between the current value and past values of the time series. It captures the effect of previous observations on the current one.\n",
    "\n",
    "2. **Integrated (I) Component**: The \"I\" in ARIMA stands for \"Integrated.\" This component accounts for differencing the series to achieve stationarity. Many time series datasets are non-stationary, meaning they have trends, seasonality, or other patterns that change over time. Differencing involves subtracting the previous observation from the current observation to remove trends and make the series stationary.\n",
    "\n",
    "3. **Moving Average (MA) Component**: Similar to the MA component in ARMA models, this part models the relationship between the current value and past error terms. It captures the influence of past forecast errors on the current value.\n",
    "\n",
    "An ARIMA(p, d, q) model is defined by these three components:\n",
    "\n",
    "- $p$ represents the order of the AutoRegressive (AR) component.\n",
    "- $d$ represents the degree of differencing required to make the series stationary.\n",
    "- $q$ represents the order of the Moving Average (MA) component.\n",
    "\n",
    "The equation for an ARIMA(p, d, q) model can be written as:\n",
    "\n",
    "$y_t' = c + \\phi_1 y_{t-1}' + \\phi_2 y_{t-2}' + \\ldots + \\phi_p y_{t-p}' + \\theta_1 \\varepsilon_{t-1} + \\theta_2 \\varepsilon_{t-2} + \\ldots + \\theta_q \\varepsilon_{t-q} + \\varepsilon_t$\n",
    "\n",
    "Where:\n",
    "- $y_t'$ is the differenced series.\n",
    "- $c$ is a constant term.\n",
    "- $\\phi_1$, $\\phi_2$, $\\ldots$, $\\phi_p$ are the autoregressive coefficients.\n",
    "- $\\theta_1$, $\\theta_2$, $\\ldots$, $\\theta_q$ are the moving average coefficients.\n",
    "- $\\varepsilon_t$ represents the white noise error term.\n",
    "\n",
    "To fit an ARIMA model to a time series dataset, you typically start by identifying the appropriate values of $p$, $d$, and $q$, often through techniques like autocorrelation and partial autocorrelation plots. Then, you can use methods like maximum likelihood estimation to estimate the model parameters and make forecasts."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the sales data of Office Supplies\n",
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df = df[df['Category']=='Furniture']\n",
    "df = df.groupby(by='Order Date').agg({'Sales':sum})\n",
    "df.sort_index(inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.resample('MS').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>6242.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>1839.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>14573.956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>7944.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>6912.787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Order Date           \n",
       "2014-01-01   6242.525\n",
       "2014-02-01   1839.658\n",
       "2014-03-01  14573.956\n",
       "2014-04-01   7944.837\n",
       "2014-05-01   6912.787"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "formatter = ticker.StrMethodFormatter('{x:,.0f}')\n",
    "\n",
    "title = 'Furniture Sales Data'\n",
    "ylabel='Sales Data'\n",
    "xlabel='Order Date' \n",
    "\n",
    "ax = df['Sales'].plot(figsize=(12,5),title=title)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)\n",
    "ax.yaxis.set_major_formatter(formatter);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETS Decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "result = seasonal_decompose(df['Sales'], model='additive')  # model='add' also works\n",
    "result.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the seasonal component does not contribute significantly to the behavior of the series.\n",
    "#### Use <tt>pmdarima.auto_arima</tt> to determine ARIMA Orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>48</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)</td> <th>  Log Likelihood     </th> <td>-502.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 26 Aug 2023</td> <th>  AIC                </th> <td>1011.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:55:36</td>     <th>  BIC                </th> <td>1017.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-2014</td>    <th>  HQIC               </th> <td>1013.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-2017</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td> 1.084e+04</td> <td> 2695.066</td> <td>    4.021</td> <td> 0.000</td> <td> 5554.237</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.3056</td> <td>    0.131</td> <td>    2.328</td> <td> 0.020</td> <td>    0.048</td> <td>    0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 7.318e+07</td> <td>    0.160</td> <td> 4.56e+08</td> <td> 0.000</td> <td> 7.32e+07</td> <td> 7.32e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>3.70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th> <td>0.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.88</td> <th>  Skew:              </th> <td>0.64</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.22</td> <th>  Kurtosis:          </th> <td>2.54</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 2.09e+24. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   48\n",
       "Model:               SARIMAX(1, 0, 0)   Log Likelihood                -502.820\n",
       "Date:                Sat, 26 Aug 2023   AIC                           1011.640\n",
       "Time:                        19:55:36   BIC                           1017.253\n",
       "Sample:                    01-01-2014   HQIC                          1013.761\n",
       "                         - 12-01-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   1.084e+04   2695.066      4.021      0.000    5554.237    1.61e+04\n",
       "ar.L1          0.3056      0.131      2.328      0.020       0.048       0.563\n",
       "sigma2      7.318e+07      0.160   4.56e+08      0.000    7.32e+07    7.32e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 3.70\n",
       "Prob(Q):                              0.98   Prob(JB):                         0.16\n",
       "Heteroskedasticity (H):               1.88   Skew:                             0.64\n",
       "Prob(H) (two-sided):                  0.22   Kurtosis:                         2.54\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 2.09e+24. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df['Sales'], seasonal=False).summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests that we should fit an `ARIMA(1,0,0)` model to best forecast future values of the series. Before we train the model, let's look at augmented Dickey-Fuller Test, and the ACF/PACF plots to see if they agree. These steps are optional, and we would likely skip them in practice."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Augmented dickey-fuller test on first difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: Furniture Sales Data\n",
      "ADF test statistic     -1.147459e+01\n",
      "p-value                 5.167971e-21\n",
      "# lags used             1.000000e+01\n",
      "# observations          3.600000e+01\n",
      "critical value (1%)    -3.626652e+00\n",
      "critical value (5%)    -2.945951e+00\n",
      "critical value (10%)   -2.611671e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.statespace.tools import diff\n",
    "\n",
    "df['d1'] = diff(df['Sales'], k_diff=1)\n",
    "\n",
    "adf_test(df['d1'], 'Furniture Sales Data')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the ACF and PACF plots\n",
    "\n",
    "A <strong>PACF Plot</strong> can reveal recommended AR(p) orders, and an <strong>ACF Plot</strong> can do the same for MA(q) orders.<br>\n",
    "Alternatively, we can compare the stepwise <a href='https://en.wikipedia.org/wiki/Akaike_information_criterion'>Akaike Information Criterion (AIC)</a> values across a set of different (p,q) combinations to choose the best combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H3JvFrFM4EVjLRAQ/fmaPk0ADFArBxq27eOL5fRy7aA6rV/TT49+GtklyIrCWefy5few+kGt3GF2vUAg+9PVH2DK4l+Fcgb5pPZxw9Fz++LyTnQxsUjz6qLXEjhcPMLh7qN1hTAkbt+5iy+BehnIFAhjKFdgyuJeNW3e1OzTrUk4E1nS79g/z5PP72x3GlPHE8/uOuGN4OFfgief3tSki63ZOBNZUB4bzbB7c6zFmGujYRXPomzb6q9s3rYdjF81pU0TW7ZwIrGlG8gV+9PRu97lvsNUr+jnh6LkobQ6YkbYRrF7R39a4rHs5EVjTuIdQc/T0iD8+72SW9c9i8dw+fvesE91QbHVxryFrmIhk2IXiGDjuIdQ8PT1i3sxpzJs5jdNWLRh7A7ManAgyJCIYKv7AR/qDH+Vj0B+5TcmPiETpvBj1gyHFMXcA9g07AZh1EyeCKaZQCPaP5Nk/nGP/UJ6DufzhX3fKua7ezI7kRNCh8oX05/pyhWRkyyrrRSRj9xwYybNvKJf0Lffx3swmwIkglS8E9zw6yKandvOKY+Zz5klH09vgxreIODRk8UguGCkUDo1kOZQe9Iu/01r+Y9hmZs3iRECSBN55471s3LqLA8N5ZvX1snpFP7esOeNQMjg4kufgSP7QePHF32MtpPXlxaGNi8Mfj3pEkE+HMPbZupl1GicC4J5HB9m4dRf7h/MA7B/O84Otu/ji97fxc8v72Ts04vp1M5uyfB8B8MC2FzmQJoGig8N5vveTF3hh37CTgJlNaZm+Ijg4kudHT++hr7eHvmk9DJWM3+Jb9s0sKxpyRSDpXEmPStoi6coKy98n6WFJD0i6S9KqkmV5SRvTx/pGxDNeuUJwYDjvW/bNLNPqviKQ1At8AjgH2AbcJ2l9RDxcstoPgIGI2C/pd4CPAr+aLjsQEavrjaMexVv2r/jiAwyN5LnsPx3nH/ows8xoxBXB6cCWiHg8IoaBzwMXlK4QEd+KiOI4xN8FljfgdRuqeMv+UfNmcNqqBU4CZpYZjUgEy4CtJdPb0nnVrAG+XjI9U9IGSd+VdGG1jSStTdfb8Oyzz9YVsJmZHdbSxmJJvwEMAK8rmb0qIrZLOh64W9KDEfFY+bYRsQ5YBzAwMOBuPGZmDdKIK4LtwIqS6eXpvFEknQ18ADg/Ig79ZmFEbE//Pg7cA5zagJjMzGycGpEI7gNOlHScpD7gImBU7x9JpwKfJEkCgyXzF0iakT4/Cng1UNrIbGZmTVZ31VBE5CRdDtwJ9AI3RcQmSdcCGyJiPfDnwFzgH5X00fxpRJwPnAx8UlKBJCldX9bbyMysrQqFYOPWXTzx/D6OXTRnSvYobEgbQUTcAdxRNu/qkudnV9nu28ArGxGDtU4WvhhmkHzWP/T1R9gyuJfhXIG+9B6jqfaLcJm+s9gmrt4vhpOIdZONW3exZXDvoVEHhnIFtgzuZePWXVPql+GcCGxC6vliZOXsyqaOJ57fx3Bu9O9uD+cKPPH8vimVCDzonE1IrS/GWEqTSDA6iZh1omMXzaFv2ujD5FQch8yJwCakni9GPUnErB2yMg6ZE4FNSD1fjKycXTVKoRB8/8mdfPH72/j+kzsp+FfrWq44Dtmy/lksntvH75514pSsynQbQQNkqQG0ngH6iknk4R27iZi6Z1eN4PaUzlEch2zezGlTql2glBNBnbL4hZ3sF8OjvI5fVnqrWGdw1VCd3AA6MR7ldXzcnmKt5ERQJ39hrRncnmKt5EQwDrUa7fyFtWbISm8V6wxuIxjDWG0AbgC1ZnB7SutkqbNHNU4EYxir0c5f2MbxF3K0enqruCzHJ4udPSpxIhjDeG4xz0L3smbzF7JxXJbj595ZCbcRjMFtAK3h3leNM1ZZ+ka1w9zZI+ErgjG4DaA1sjK4VyvUKsvVK/p9tVCieKI3VFJejTzR65YqOieCMbSzDaBbPkSNMJ4vZJbKox61ytJVIaM180Svm6ronAjGoR1tAN30IWqEsb6QWSuPetQqyy9v3O4rrxL1nujVOjnppqTrRNChmv0h6rSz67G+kN30pWq3WmXZ7KqQbjTZE72xTk66qbqzIY3Fks6V9KikLZKurLB8hqTb0uX3Sjq2ZNlV6fxHJb2xEfFMBc1sxCp+gD9292a+cP82Pnb3Zj709Ufa3mhYa/gJN+pNTLWy9I1qjTNWo3w3dTSpOxFI6gU+AZwHnAJcLOmUstXWADsj4gTgBuAj6banABcBrwDOBf463V/mNfND1I09dLrpS9XJxjOssnsVjc9YJydjJd1OKmdF1Pfikl4FXBMRb0ynrwKIiA+XrHNnus53JE0DngYWA1eWrlu6Xq3XXLjq5Djnj2+acKwP79gNwClL5wOQj2DfUO7Q8ief3w/AqkWzj9i21rLxLJ+oiOCnLxxg/3AeAAlmTe9l5cJZSPVV4Ty7Z4jn9g4fMX/x3D6OmjdjXPtoVllVW9bM8uhk7SrnAyN5IlzOtZbvOZhj+64DlB5CJVjWP4t5M5Na94jgJ8/tpxDBkvkzmTujF0mTLudZ03uZ3jv58/fb3/2f7o+IgfL5jWgjWAZsLZneBpxRbZ2IyEl6EViUzv9u2bbLKr2IpLXAWoC5S182qUCLCaCaWgfxsQ7wtZZP5kMmiZULZ7F3KM/QSJ4Z03sPfYjGu+9qy2ZO70XiiA/wjOm9Y25b1KyyqrZsPOXRjANju7dtdTnvHcofOjhB8hk5MJJn71D+0MGtXWXVzH1Ppiznzuhl1vTeIw7mc2cc/h5J4vjFR161jqecx/OeGqVrGosjYh2wDmBgYCBu++1X1b3PvUM5Htz2Yt37Gcu1X90EwNVvfsWklk9239WWjacHTj0xtUs95dyp27baF7+/jS/cv230zIBXHb+It562HGhfWdVaXigEV3zxAQ6O5HnzK4+p2PmhGWU92U4X4ynnSjEfv3gOS+bPnHS8t7+78vxGJILtwIqS6eXpvErrbEurhl4CPD/Oba3BivXEndRrqJ0KhWDPwRwHR/J8/8mdDS2LZu67GbqxV1HxxKZYTfOxuze3rGtxT484bdWCCfcC6rRybkSvofuAEyUdJ6mPpPF3fdk664FL0+dvA+6OpHFiPXBR2qvoOOBE4HsNiMnGUPwAv/W05Zn+gZjSg8hze4cb2oOqmftulmID54xpPYju6FVU7PxQrGbphs4PnVbOdV8RpHX+lwN3Ar3ATRGxSdK1wIaIWA/cCNwiaQvwAkmyIF3vduBhIAe8JyLy9cZk9em2s9h61DqI1NvXu5n7bpZmXi0263PVTf31izrtqrwhbQQRcQdwR9m8q0ueHwTeXmXb64DrGhGH1a+dl9nt0MyDSDceoGDy1R21NPNz1WnVLOPVjHKedCztDsA6SzdeZtejmfcn+N6Hw5r5ueq0apZu1DW9hqw1uvUsdrKKB5HyHlTjPYjUqu6od99TSSM+V9XKutOqWbqRE4GN0q2X2ZNVz0FkrOqOLB6gqh2s6/1cjaesO6WapRu5ashGmaqX2cUD1LN7ho64nX+yPajGU91Ra9+1YupGtXpJ1fu5ylqVJbT28+ErAhtlKp7FNquhsp7qjqnYKD9WL6l6Plf1Vi11W0+4ap+PT11yxOgQDeErAjvCVLvHoFlnk/U0Bk/FM9yxBmGr53NVT1l34/0c1T4f33ns+aa8nhOBTXnNGsK6nuqOqTisdjN7SdVT1t2YdKt9Pn78zJ6mvJ6rhmzKa1YDeD3VaFOxUb6ZvaTqKetu7AlX7fPxM0vmNeX1nAiarNvqJrtVu7pxTra3ylTsWtrs9qWpMq7PeFT7fLzqZYua8npOBE00FRsEO1E3duPsxJgaoRO7cXZj0q32+eht0ufDiaCJxjPWTLOuGLJ0JTKecu7EA1QnxjQVdWvSbeXnw43FTTRWg2CzejN0Yy+JekzFhldrrKnWE67RnAiaaKxeFM3qzdCNvSTq0ewxfabajV/1cFlMTU4ETTRWl7dmnclm7Qy5mXdDZ+3qqhaXxdTlNoImGqtuslm9Gbqxl0Q9mlkH3I2/KdAszS6LLLVrdRpfETRZrbrJZp3JTtXxgmppVh1w1q6uamlmWfhqo718RdBG9Z7Jelje5sva1VUtzSwLX3m1lxNBm022i5iH5W2NbuyD3izNLItuvPt3KqkrEUhaCNwGHAs8AbwjInaWrbMa+BtgPpAHrouI29JlnwFeB7yYrn5ZRGysJ6as8BlUa/jq6rBmloWvvNqr3iuCK4G7IuJ6SVem01eUrbMfuCQiNks6Brhf0p0RsStd/ocR8YU648gcn0G1jq+uDmtWWfjKq73qTQQXAGemz28G7qEsEUTEj0uePyVpEFgM7KrztTPNZ1A2lfjKq73q7TW0JCJ2pM+fBpbUWlnS6UAf8FjJ7OskPSDpBkkzamy7VtIGSRueffbZOsPuflnsGWRTm+/+bZ8xrwgkfRN4aYVFHyidiIiQVLWvl6SlwC3ApRFRPI29iiSB9AHrSK4mrq20fUSsS9dhYGAg833KfAZlZo0yZiKIiLOrLZP0jKSlEbEjPdAPVllvPvA14AMR8d2SfRevJoYk/R3w/glFn3GuuzazRqi3amg9cGn6/FLgK+UrSOoDvgR8trxROE0eSBJwIfBQnfFMSI+Sh5lZltXbWHw9cLukNcCTwDsAJA0A746Id6XzXgssknRZul2xm+jnJC0GBGwE3l1nPBMyu28av3jsQvYN59hzsPgYYSSf+ZonM8sQRXTfQW9gYCA2bNjQtP0fHMmz++DIoeRwYDjftNcyMxuv4xfPYcn8mZPeXtL9ETFQPt93Flcwc3ovM6f3cnT686Aj+QJ7D+Y4mMtTCA6Nf1KIoBAQ6d9CBLl8JH8LQb7kYWbWqZwIxmF6bw8L5vTVtY9cvsBIPhjOFw49H8kXGMkXyBWC4VyBoVwy3YUXaWbWxZwIWmRabw/TemEWvTXXi0iSxUg+SQ7DuQK5Qu3kcHAkz/7hPAdG8k4iZjZhTgQdRhIzpvUyYxpQ9fa6ygqF4EAxKQzn2TecjEw6nCvg2ikzq8aJYArp6RFzZkxjzowj/1tz+QLD+eQKo/h3JB/U6ixQXJKsEkQk85K/SXvIUC6p6nKiMeteTgQZkVRN9TC7vqaOqortHSO5YCif57k9w7x4YKQ5L2ZmDeVEYA0xvbeH6b09yWAhTGfh7D4eemq3u96adQH/VKU1xbTeHl7+0nlM7/Wt22adzonAmmbm9F5OXDIPOReYdTQnAmuql8yaznFH+TcSzDqZE4E13ZL5M1n6ksnfFm9mzeVEYC2xatFs+mdPb3cYZlaBE4G1hCROPHous/pq31ltZq3nRGAt455EZp3J9xFYS82c3stJL53Hzn0j5ArJgHu5fIx67tFazVrLicBabt7M6cybWbu9IKJ0SIskMRSHtzi0DkcmjAjIFw4PA54rFCgUIFcoHBoSvHzo8EL6WoWSnY9+nUS+UByWY5Jv3KxDORFYR5JUcv9B51QlRcSoIcSLI8Xm0uHDCxGHklcynSSs8uRRntAiDv++RZKYigkLXyFZ0zkRmE2AJPqmib5prW1eKxSCfBSvaJIkkU9/x+K5vUO8eGDEVyo2aXUlAkkLgduAY4EngHdExM4K6+WBB9PJn0bE+en844DPA4uA+4F3RsRwPTGZTUU9PaIHMb1Cp6vF82YwlMszuHuIZ/cOMTRSaH2A1tXqPa25ErgrIk4E7kqnKzkQEavTx/kl8z8C3BARJwA7gTV1xmOWSTOm9bJi4WxOXdHPyUvnsWhuHz2dU6NmHa7eqqELgDPT5zcD9wBXjGdDSQLOAn6tZPtrgL+pMyazzJJE/+w++mf3MZIvcGAkGf01gkOt3qVtFqW1SaW/TTFWLVNE0gCfyxd/Ua/kuX8IqevUmwiWRMSO9PnTwJIq682UtAHIAddHxJdJqoN2RUQuXWcbsKzaC0laC6wFWLlyZZ1hm019h4YGb4NCIWk0L+2RVfxBo2KSiIjRP36ULs8XigklGM7nGc6NTjbWeGMmAknfBF5aYdEHSiciIiRV+19aFRHbJR0P3C3pQeDFiQQaEeuAdQADAwP+NJh1sJ60Xqq3wT2+CmljebErcbHLbzHJHNE7q3z7CKKQ/M2XdR0udise1aOrZL+5QrGH2Oj7XqaCMRNBRJxdbZmkZyQtjYgdkpYCg1X2sT39+7ike4BTgX8C+iVNS68KlgPbJ/EezCwjio3mnaLYnbhQloHG6sFV6R6Y8WjWFV69VUPrgUuB69O/XylfQdICYH9EDEk6Cng18NH0CuJbwNtIeg5V3N7MrFMVuxN3u3rTy/XAOZI2A2en00gakPTpdJ2TgQ2Sfgh8i6SN4OF02RXA+yRtIWkzuLHOeMzMbIIUXXgXysDAQGzYsKHdYZiZdRVJ90fEQPl8jz5qZpZxTgRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZ50RgZpZxTgRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZ50RgZpZxTgRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZV1cikLRQ0jckbU7/Lqiwzn+RtLHkcVDShemyz0j6Scmy1fXEY2ZmE1fvFcGVwF0RcSJwVzo9SkR8KyJWR8Rq4CxgP/AvJav8YXF5RGysMx4zM5ugehPBBcDN6fObgQvHWP9twNcjYn+dr2tmZg1SbyJYEhE70udPA0vGWP8i4NayeddJekDSDZJm1BmPmZlN0LSxVpD0TeClFRZ9oHQiIkJS1NjPUuCVwJ0ls68iSSB9wDrgCuDaKtuvBdYCrFy5cqywzcxsnMZMBBFxdrVlkp6RtDQidqQH+sEau3oH8KWIGCnZd/FqYkjS3wHvrxHHOpJkwcDAQNWEY2ZmE1Nv1dB64NL0+aXAV2qsezFl1UJp8kCSSNoXHqozHjMzm6B6E8H1wDmSNgNnp9NIGpD06eJKko4FVgD/Wrb95yQ9CDwIHAX8WZ3xmJnZBI1ZNVRLRDwPvL7C/A3Au0qmnwCWVVjvrHpe38zM6uc7i83MMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMq6uRCDp7ZI2SSpIGqix3rmSHpW0RdKVJfOPk3RvOv82SX31xGNmZhNX7xXBQ8BbgX+rtoKkXuATwHnAKcDFkk5JF38EuCEiTgB2AmvqjMfMzCaorkQQEY9ExKNjrHY6sCUiHo+IYeDzwAWSBJwFfCFd72bgwnriMTOziZvWgtdYBmwtmd4GnAEsAnZFRK5k/rJqO5G0FlibTu6VNFYCquYo4LlJbttMnRhXJ8YEnRlXJ8YEnRlXJ8YE2YhrVaWZYyYCSd8EXlph0Qci4iv1RjVeEbEOWFfvfiRtiIiq7Rnt0olxdWJM0JlxdWJM0JlxdWJMkO24xkwEEXF2na+xHVhRMr08nfc80C9pWnpVUJxvZmYt1Iruo/cBJ6Y9hPqAi4D1ERHAt4C3petdCrTsCsPMzBL1dh99i6RtwKuAr0m6M51/jKQ7ANKz/cuBO4FHgNsjYlO6iyuA90naQtJmcGM98YxT3dVLTdKJcXViTNCZcXViTNCZcXViTJDhuJScmJuZWVb5zmIzs4xzIjAzy7hMJYJqQ120k6QnJD0oaaOkDW2M4yZJg5IeKpm3UNI3JG1O/y7okLiukbQ9LbONkt7U4phWSPqWpIfTIVZ+L53ftvKqEVO7y2qmpO9J+mEa1wfT+W0bXqZGTJ+R9JOSslrdqpjK4uuV9ANJX02nm19WEZGJB9ALPAYcD/QBPwRO6YC4ngCO6oA4XgucBjxUMu+jwJXp8yuBj3RIXNcA729jWS0FTkufzwN+TDJ8StvKq0ZM7S4rAXPT59OBe4FfAm4HLkrn/y3wOx0Q02eAt7WrrEriex/wD8BX0+mml1WWrggqDnXR5pg6RkT8G/BC2ewLSIb+gDYNAVIlrraKiB0R8f30+R6S3nDLaGN51YiprSKxN52cnj6CNg4vUyOmtpO0HPhl4NPpdEuG4slSIqg01EXbvygkH8B/kXR/OoxGJ1kSETvS508DS9oZTJnLJT2QVh21vMqqSNKxwKkkZ5UdUV5lMUGbyyqt6tgIDALfILky3xXjHF6mFTFFRLGsrkvL6gZJM1oZU+r/AH8EFNLpCQ3FM1lZSgSd6jURcRrJ6KzvkfTadgdUSSTXpR1x1gT8DfAyYDWwA/iLdgQhaS7wT8DvR8Tu0mXtKq8KMbW9rCIiHxGrSUYPOB14eatjKFcek6SfBa4iie0XgYUk9zm1jKQ3A4MRcX8rXxeylQiqDXXRVhGxPf07CHyJ5IvSKZ6RtBQg/TvY5ngAiIhn0i9yAfgUbSgzSdNJDrifi4gvprPbWl6VYuqEsiqKiF0kowm8inR4mXRR276LJTGdm1avRUQMAX9H68vq1cD5kp4gqbo+C/grWlBWWUoEFYe6aGdAkuZImld8DryB5DceOsV6kqE/oIOGACkebFNvocVlltbb3gg8EhF/WbKobeVVLaYOKKvFkvrT57OAc0jaL9o2vEyVmH5UksRFUg/f0rKKiKsiYnlEHEtyfLo7In6dVpRVu1vIW/kA3kTSm+IxktFT2x3P8SS9l34IbGpnTMCtJFUHIyT1kGtI6ifvAjYD3wQWdkhctwAPAg+QHHyXtjim15BU+zwAbEwfb2pnedWIqd1l9XPAD9LXfwi4Op1/PPA9YAvwj8CMDojp7rSsHgL+nrRnUTsewJkc7jXU9LLyEBNmZhmXpaohMzOrwInAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwy7v8D/KFw4JTvaasAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Furniture Monthly Sales Data'\n",
    "lags = 40\n",
    "plot_acf(df['Sales'],title=title,lags=lags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Partial Autocorrelation: Monthly Furniture Sales Data'\n",
    "lags = 12\n",
    "plot_pacf(df['Sales'],title=title,lags=lags)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us that the AR component should be more important than MA. From the <a href='https://people.duke.edu/~rnau/411arim3.htm'>Duke University Statistical Forecasting site</a>:<br>\n",
    "> <em>If the PACF displays a sharp cutoff while the ACF decays more slowly (i.e., has significant spikes at higher lags), we    say that the stationarized series displays an \"AR signature,\" meaning that the autocorrelation pattern can be explained more    easily by adding AR terms than by adding MA terms.</em><br>\n",
    "\n",
    "Let's take a look at <tt>pmdarima.auto_arima</tt> done stepwise to see if having $p$ and $q$ terms the same still makes sense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=1078.259, Time=0.05 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=1026.129, Time=0.01 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0]             : AIC=1059.367, Time=0.01 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0]             : AIC=1022.619, Time=0.01 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0]             : AIC=1018.040, Time=0.03 sec\n",
      " ARIMA(1,0,1)(0,0,0)[0]             : AIC=1018.347, Time=0.02 sec\n",
      " ARIMA(2,0,2)(0,0,0)[0]             : AIC=1020.706, Time=0.05 sec\n",
      " ARIMA(1,0,2)(0,0,0)[0]             : AIC=1019.172, Time=0.02 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=1015.561, Time=0.02 sec\n",
      " ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=1013.580, Time=0.01 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=1011.909, Time=0.01 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=1014.386, Time=0.01 sec\n",
      " ARIMA(0,0,2)(0,0,0)[0] intercept   : AIC=1013.938, Time=0.01 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=1011.640, Time=0.01 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0] intercept   : AIC=1013.680, Time=0.01 sec\n",
      "\n",
      "Best model:  ARIMA(1,0,0)(0,0,0)[0] intercept\n",
      "Total fit time: 0.304 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>48</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)</td> <th>  Log Likelihood     </th> <td>-502.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 26 Aug 2023</td> <th>  AIC                </th> <td>1011.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:13:36</td>     <th>  BIC                </th> <td>1017.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-2014</td>    <th>  HQIC               </th> <td>1013.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-2017</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td> 1.084e+04</td> <td> 2695.066</td> <td>    4.021</td> <td> 0.000</td> <td> 5554.237</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.3056</td> <td>    0.131</td> <td>    2.328</td> <td> 0.020</td> <td>    0.048</td> <td>    0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 7.318e+07</td> <td>    0.160</td> <td> 4.56e+08</td> <td> 0.000</td> <td> 7.32e+07</td> <td> 7.32e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>3.70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th> <td>0.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.88</td> <th>  Skew:              </th> <td>0.64</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.22</td> <th>  Kurtosis:          </th> <td>2.54</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 2.09e+24. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   48\n",
       "Model:               SARIMAX(1, 0, 0)   Log Likelihood                -502.820\n",
       "Date:                Sat, 26 Aug 2023   AIC                           1011.640\n",
       "Time:                        20:13:36   BIC                           1017.253\n",
       "Sample:                    01-01-2014   HQIC                          1013.761\n",
       "                         - 12-01-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   1.084e+04   2695.066      4.021      0.000    5554.237    1.61e+04\n",
       "ar.L1          0.3056      0.131      2.328      0.020       0.048       0.563\n",
       "sigma2      7.318e+07      0.160   4.56e+08      0.000    7.32e+07    7.32e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 3.70\n",
       "Prob(Q):                              0.98   Prob(JB):                         0.16\n",
       "Heteroskedasticity (H):               1.88   Skew:                             0.64\n",
       "Prob(H) (two-sided):                  0.22   Kurtosis:                         2.54\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 2.09e+24. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_fit = auto_arima(df['Sales'], start_p=0, start_q=0,\n",
    "                          max_p=2, max_q=2, m=12,\n",
    "                          seasonal=False,\n",
    "                          d=None, trace=True,\n",
    "                          error_action='ignore',   # we don't want to know if an order does not work\n",
    "                          suppress_warnings=True,  # we don't want convergence warnings\n",
    "                          stepwise=True)           # set to stepwise\n",
    "\n",
    "stepwise_fit.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = len(df)\n",
    "test_size = 12\n",
    "train = df.iloc[:n-test_size]\n",
    "test = df.iloc[n-test_size:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit an ARIMA(1,0,0) Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Sales</td>      <th>  No. Observations:  </th>    <td>36</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-376.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 26 Aug 2023</td> <th>  AIC                </th>  <td>758.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:21:06</td>     <th>  BIC                </th>  <td>762.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-2014</td>    <th>  HQIC               </th>  <td>759.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-01-2016</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td> 1.463e+04</td> <td> 1934.689</td> <td>    7.561</td> <td> 0.000</td> <td> 1.08e+04</td> <td> 1.84e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.2825</td> <td>    0.170</td> <td>    1.658</td> <td> 0.097</td> <td>   -0.051</td> <td>    0.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 6.929e+07</td> <td>    0.134</td> <td> 5.15e+08</td> <td> 0.000</td> <td> 6.93e+07</td> <td> 6.93e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>4.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.92</td> <th>  Prob(JB):          </th> <td>0.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.40</td> <th>  Skew:              </th> <td>0.83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.57</td> <th>  Kurtosis:          </th> <td>2.53</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 5.34e+23. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   No. Observations:                   36\n",
       "Model:                 ARIMA(1, 0, 0)   Log Likelihood                -376.058\n",
       "Date:                Sat, 26 Aug 2023   AIC                            758.116\n",
       "Time:                        20:21:06   BIC                            762.867\n",
       "Sample:                    01-01-2014   HQIC                           759.774\n",
       "                         - 12-01-2016                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1.463e+04   1934.689      7.561      0.000    1.08e+04    1.84e+04\n",
       "ar.L1          0.2825      0.170      1.658      0.097      -0.051       0.616\n",
       "sigma2      6.929e+07      0.134   5.15e+08      0.000    6.93e+07    6.93e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                 4.47\n",
       "Prob(Q):                              0.92   Prob(JB):                         0.11\n",
       "Heteroskedasticity (H):               1.40   Skew:                             0.83\n",
       "Prob(H) (two-sided):                  0.57   Kurtosis:                         2.53\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 5.34e+23. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(train['Sales'], order=(1, 0, 0))\n",
    "\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain predicted values\n",
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions = results.predict(start=start, end=end, dynamic=False, typ='levels').rename('ARIMA(1,0,0) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-01    20856.524035\n",
       "2017-02-01    16387.395968\n",
       "2017-03-01    15125.048311\n",
       "2017-04-01    14768.486230\n",
       "2017-05-01    14667.771885\n",
       "2017-06-01    14639.324162\n",
       "2017-07-01    14631.288832\n",
       "2017-08-01    14629.019177\n",
       "2017-09-01    14628.378091\n",
       "2017-10-01    14628.197011\n",
       "2017-11-01    14628.145863\n",
       "2017-12-01    14628.131415\n",
       "Freq: MS, Name: ARIMA(1,0,0) Predictions, dtype: float64"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passing <tt>dynamic=False</tt> means that forecasts at each point are generated using the full history up to that point (all lagged values).\n",
    "\n",
    "Passing <tt>typ='levels'</tt> predicts the levels of the original endogenous variables. If we'd used the default <tt>typ='linear'</tt> we would have seen linear predictions in terms of the differenced endogenous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=20856.52404, expected=5964.032\n",
      "predicted=16387.39597, expected=6866.3374\n",
      "predicted=15125.04831, expected=10893.4448\n",
      "predicted=14768.48623, expected=9065.9581\n",
      "predicted=14667.77188, expected=16957.5582\n",
      "predicted=14639.32416, expected=19008.5867\n",
      "predicted=14631.28883, expected=11813.021999999999\n",
      "predicted=14629.01918, expected=15441.874\n",
      "predicted=14628.37809, expected=29028.206000000002\n",
      "predicted=14628.19701, expected=21884.0682\n",
      "predicted=14628.14586, expected=37056.715\n",
      "predicted=14628.13142, expected=31407.4668\n"
     ]
    }
   ],
   "source": [
    "# Compare predictions to expected values\n",
    "for i in range(len(predictions)):\n",
    "    print(f\"predicted={predictions[i]:<11.10}, expected={test['Sales'][i]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "title = 'Monthly Sales of Furnitures'\n",
    "ylabel='Sales Data'\n",
    "xlabel='Order Date'\n",
    "\n",
    "ax = test['Sales'].plot(legend=True,figsize=(12,6),title=title)\n",
    "predictions.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)\n",
    "ax.yaxis.set_major_formatter(formatter)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1,0,0) MSE Error: 120032385.7\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "error = mean_squared_error(test['Sales'], predictions)\n",
    "print(f'ARIMA(1,0,0) MSE Error: {error:11.10}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(1,0,0) RMSE Error: 10955.92925\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "error = rmse(test['Sales'], predictions)\n",
    "print(f'ARIMA(1,0,0) RMSE Error: {error:11.10}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrain the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARIMA(df['Sales'],order=(1,1,1))\n",
    "results = model.fit()\n",
    "fcast = results.predict(len(df),len(df)+11,typ='levels').rename('ARIMA(1,0,0) Forecast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "title = 'Monthly Sales Data of Furniture'\n",
    "ylabel='Sales Data'\n",
    "xlabel='Order Date'\n",
    "\n",
    "ax = df['Sales'].plot(legend=True,figsize=(12,6),title=title)\n",
    "fcast.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)\n",
    "ax.yaxis.set_major_formatter(formatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seasonal Autoregressive Integreted Moving Averages(SARIMA)\n",
    "\n",
    "It's an extension of the ARIMA model that includes additional components to capture seasonality in time series data. SARIMA models are particularly useful for analyzing and forecasting data with recurring patterns that repeat over fixed intervals.\n",
    "\n",
    "SARIMA models build upon the concepts of ARIMA models, with the addition of seasonal components:\n",
    "\n",
    "1. **Seasonal AutoRegressive (SAR) Component**: This component captures the relationship between the current value and past values at the same seasonal lag. It's similar to the AR component but operates over the seasonal interval.\n",
    "\n",
    "2. **Seasonal Integrated (SI) Component**: Just like the non-seasonal integrated component in ARIMA, this component accounts for differencing the series at the seasonal interval to achieve seasonal stationarity.\n",
    "\n",
    "3. **Seasonal Moving Average (SMA) Component**: This component captures the relationship between the current value and past error terms at the same seasonal lag. It's similar to the MA component but operates over the seasonal interval.\n",
    "\n",
    "A SARIMA(p, d, q) × (P, D, Q, s) model is defined by these components:\n",
    "\n",
    "- $p$ is the order of the non-seasonal AutoRegressive (AR) component.\n",
    "- $d$ is the degree of non-seasonal differencing.\n",
    "- $q$ is the order of the non-seasonal Moving Average (MA) component.\n",
    "- $P$ is the order of the seasonal AutoRegressive (SAR) component.\n",
    "- $D$ is the degree of seasonal differencing.\n",
    "- $Q$ is the order of the seasonal Moving Average (SMA) component.\n",
    "- $s$ is the number of time steps in each seasonal period.\n",
    "\n",
    "The equation for a SARIMA(p, d, q) × (P, D, Q, s) model can be quite complex and includes both non-seasonal and seasonal components.\n",
    "\n",
    "*SARIMA* models are particularly effective for time series datasets that exhibit seasonality, trends, and other complex patterns. These models allow you to capture and model the behavior of the data over multiple time scales, helping to improve forecasting accuracy.\n",
    "\n",
    "To fit a SARIMA model, a similar process is followed as with ARIMA models. You need to determine the appropriate values of $p$, $d$, $q$, $P$, $D$, $Q$, and $s$ through analysis of autocorrelation and partial autocorrelation plots, as well as seasonal decomposition techniques. Once the model is specified, parameter estimation methods such as maximum likelihood estimation are used to fit the model to the data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-info\"><h4>Related Functions:</h4>\n",
    "<tt><strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html'>sarimax.SARIMAX</a></strong><font color=green>(endog[, exog, order, …])</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.vector_ar.var_model.VARResults.html'>sarimax.SARIMAXResults</a></strong><font color=green>(model, params, …[, …])</font>&nbsp;&nbsp;Class to hold results from fitting a SARIMAX model.</tt>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# Load specific forecasting tools\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf # for determining (p,q) orders\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose      # for ETS Plots\n",
    "from pmdarima import auto_arima                              # for determining ARIMA orders\n",
    "\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the sales data of Office Supplies\n",
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df = df[df['Category']=='Furniture']\n",
    "df = df.groupby(by='Order Date').agg({'Sales':sum})\n",
    "df.sort_index(inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.resample('MS').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETS Decomposition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = seasonal_decompose(df['Sales'], model='add')\n",
    "result.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <tt>pmdarima.auto_arima</tt> to obtain recommended orders\n",
    "This may take awhile as there are a lot more combinations to evaluate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>             <td>y</td>          <th>  No. Observations:  </th>    <td>48</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 0, 12)</td> <th>  Log Likelihood     </th> <td>-349.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Sat, 26 Aug 2023</td>   <th>  AIC                </th>  <td>707.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>20:50:11</td>       <th>  BIC                </th>  <td>714.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>               <td>01-01-2014</td>      <th>  HQIC               </th>  <td>709.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td>- 12-01-2017</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>          <td>opg</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td> 1616.5115</td> <td> 1257.009</td> <td>    1.286</td> <td> 0.198</td> <td> -847.181</td> <td> 4080.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th>  <td>   -0.1196</td> <td>    0.087</td> <td>   -1.378</td> <td> 0.168</td> <td>   -0.290</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th>  <td>    0.1214</td> <td>    0.098</td> <td>    1.240</td> <td> 0.215</td> <td>   -0.070</td> <td>    0.313</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 1.862e+07</td> <td>    0.036</td> <td>  5.1e+08</td> <td> 0.000</td> <td> 1.86e+07</td> <td> 1.86e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.71</td> <th>  Jarque-Bera (JB):  </th> <td>1.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.40</td> <th>  Prob(JB):          </th> <td>0.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.96</td> <th>  Skew:              </th> <td>0.55</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.95</td> <th>  Kurtosis:          </th> <td>2.84</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 1.55e+25. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                SARIMAX Results                                 \n",
       "================================================================================\n",
       "Dep. Variable:                        y   No. Observations:                   48\n",
       "Model:             SARIMAX(2, 1, 0, 12)   Log Likelihood                -349.872\n",
       "Date:                  Sat, 26 Aug 2023   AIC                            707.744\n",
       "Time:                          20:50:11   BIC                            714.078\n",
       "Sample:                      01-01-2014   HQIC                           709.955\n",
       "                           - 12-01-2017                                         \n",
       "Covariance Type:                    opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   1616.5115   1257.009      1.286      0.198    -847.181    4080.205\n",
       "ar.S.L12      -0.1196      0.087     -1.378      0.168      -0.290       0.051\n",
       "ar.S.L24       0.1214      0.098      1.240      0.215      -0.070       0.313\n",
       "sigma2      1.862e+07      0.036    5.1e+08      0.000    1.86e+07    1.86e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.71   Jarque-Bera (JB):                 1.87\n",
       "Prob(Q):                              0.40   Prob(JB):                         0.39\n",
       "Heteroskedasticity (H):               0.96   Skew:                             0.55\n",
       "Prob(H) (two-sided):                  0.95   Kurtosis:                         2.84\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 1.55e+25. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For SARIMA Orders we set seasonal=True and pass in an m value\n",
    "auto_arima(df['Sales'],seasonal=True,m=12).summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set one year for testing\n",
    "train = df.iloc[:len(df)-6]\n",
    "test = df.iloc[len(df)-6:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  8.29091D+00    |proj g|=  1.36692D+00\n",
      "\n",
      "At iterate    5    f=  6.89978D+00    |proj g|=  6.06503D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f=  6.89834D+00    |proj g|=  3.91161D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     14     16      1     0     0   1.363D-05   6.898D+00\n",
      "  F =   6.8983397481910451     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                <td>Sales</td>              <th>  No. Observations:  </th>    <td>42</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 3)x(2, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-289.730</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Sat, 26 Aug 2023</td>         <th>  AIC                </th>  <td>591.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>20:52:16</td>             <th>  BIC                </th>  <td>599.664</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>01-01-2014</td>            <th>  HQIC               </th>  <td>594.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 06-01-2017</td>           <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>               <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.3976</td> <td>    0.146</td> <td>   -2.722</td> <td> 0.006</td> <td>   -0.684</td> <td>   -0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>    <td>   -0.1639</td> <td>    0.156</td> <td>   -1.053</td> <td> 0.292</td> <td>   -0.469</td> <td>    0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>    <td>   -0.1625</td> <td>    0.167</td> <td>   -0.972</td> <td> 0.331</td> <td>   -0.490</td> <td>    0.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.1664</td> <td>    0.118</td> <td>   -1.404</td> <td> 0.160</td> <td>   -0.399</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th> <td>    0.2224</td> <td>    0.130</td> <td>    1.710</td> <td> 0.087</td> <td>   -0.033</td> <td>    0.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 2.461e+07</td> <td> 5.24e-10</td> <td>  4.7e+16</td> <td> 0.000</td> <td> 2.46e+07</td> <td> 2.46e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>2.15</td> <th>  Jarque-Bera (JB):  </th> <td>0.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.14</td> <th>  Prob(JB):          </th> <td>0.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.75</td> <th>  Skew:              </th> <td>0.17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.65</td> <th>  Kurtosis:          </th> <td>2.22</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 6.01e+32. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                      \n",
       "===========================================================================================\n",
       "Dep. Variable:                               Sales   No. Observations:                   42\n",
       "Model:             SARIMAX(0, 1, 3)x(2, 1, [], 12)   Log Likelihood                -289.730\n",
       "Date:                             Sat, 26 Aug 2023   AIC                            591.461\n",
       "Time:                                     20:52:16   BIC                            599.664\n",
       "Sample:                                 01-01-2014   HQIC                           594.030\n",
       "                                      - 06-01-2017                                         \n",
       "Covariance Type:                               opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -0.3976      0.146     -2.722      0.006      -0.684      -0.111\n",
       "ma.L2         -0.1639      0.156     -1.053      0.292      -0.469       0.141\n",
       "ma.L3         -0.1625      0.167     -0.972      0.331      -0.490       0.165\n",
       "ar.S.L12      -0.1664      0.118     -1.404      0.160      -0.399       0.066\n",
       "ar.S.L24       0.2224      0.130      1.710      0.087      -0.033       0.477\n",
       "sigma2      2.461e+07   5.24e-10    4.7e+16      0.000    2.46e+07    2.46e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   2.15   Jarque-Bera (JB):                 0.87\n",
       "Prob(Q):                              0.14   Prob(JB):                         0.65\n",
       "Heteroskedasticity (H):               0.75   Skew:                             0.17\n",
       "Prob(H) (two-sided):                  0.65   Kurtosis:                         2.22\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 6.01e+32. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(train['Sales'],order=(0,1,3),seasonal_order=(2,1,0,12))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain predicted values\n",
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions = results.predict(start=start, end=end, dynamic=False, typ='levels').rename('SARIMA(0,1,3)(1,0,1,12) Predictions')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passing <tt>dynamic=False</tt> means that forecasts at each point are generated using the full history up to that point (all lagged values).\n",
    "\n",
    "Passing <tt>typ='levels'</tt> predicts the levels of the original endogenous variables. If we'd used the default <tt>typ='linear'</tt> we would have seen linear predictions in terms of the differenced endogenous variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=18982.66877, expected=11813.021999999999\n",
      "predicted=15746.93545, expected=15441.874\n",
      "predicted=29761.95657, expected=29028.206000000002\n",
      "predicted=13953.797  , expected=21884.0682\n",
      "predicted=35822.90086, expected=37056.715\n",
      "predicted=34853.24127, expected=31407.4668\n"
     ]
    }
   ],
   "source": [
    "# Compare predictions to expected values\n",
    "for i in range(len(predictions)):\n",
    "    print(f\"predicted={predictions[i]:<11.10}, expected={test['Sales'][i]}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Order Date'), Text(0, 0.5, 'Sales Data')]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "title = 'Monthly Sales of Furniture'\n",
    "ylabel='Sales Data'\n",
    "xlabel='Order Date'\n",
    "\n",
    "ax = test['Sales'].plot(legend=True,figsize=(12,6),title=title)\n",
    "predictions.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model's Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SARIMA(2,0,1,12) MSE Error: 21386691.26\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "error = mean_squared_error(test['Sales'], predictions)\n",
    "print(f'SARIMA(2,0,1,12) MSE Error: {error:11.10}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecast in future\n",
    "\n",
    "Let's retrain the model on whole data and forecast the future values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  7.34114D+00    |proj g|=  1.71764D-01\n",
      "\n",
      "At iterate    5    f=  7.33086D+00    |proj g|=  7.19168D-03\n",
      "\n",
      "At iterate   10    f=  7.32688D+00    |proj g|=  7.54832D-03\n",
      "\n",
      "At iterate   15    f=  7.32657D+00    |proj g|=  3.72058D-03\n",
      "\n",
      "At iterate   20    f=  7.32653D+00    |proj g|=  4.71292D-05\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     20     23      1     0     0   4.713D-05   7.327D+00\n",
      "  F =   7.3265270010504215     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    }
   ],
   "source": [
    "model = SARIMAX(df['Sales'],order=(1,0,2),seasonal_order=(2,1,0,12))\n",
    "results = model.fit()\n",
    "fcast = results.predict(len(df),len(df)+12,typ='levels').rename('SARIMA(0,1,3)(1,0,1,12) Forecast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Order Date'), Text(0, 0.5, 'Sales Data')]"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "# Plot predictions against known values\n",
    "title = 'Monthly Sales of Furniture'\n",
    "ylabel='Sales Data'\n",
    "xlabel='Order Date'\n",
    "\n",
    "ax = df['Sales'].plot(legend=True,figsize=(12,6),title=title)\n",
    "fcast.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SARIMAX\n",
    "\n",
    "- **SARIMAX** stands for Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors. \n",
    "- It's an extension of the SARIMA model that allows you to incorporate external factors, also known as exogenous variables or regressors, into the time series forecasting process. \n",
    "- These external variables can help improve the accuracy of forecasts by accounting for additional information that might impact the time series.\n",
    "\n",
    "SARIMAX models combine the concepts of SARIMA models with the ability to include exogenous variables. The main components of SARIMAX models are as follows:\n",
    "\n",
    "1. **Seasonal AutoRegressive (SAR) Component**: Captures the relationship between the current value and past values at the same seasonal lag, similar to the SAR component in SARIMA.\n",
    "\n",
    "2. **Seasonal Integrated (SI) Component**: Involves differencing the series at the seasonal interval to achieve seasonal stationarity, similar to the SI component in SARIMA.\n",
    "\n",
    "3. **Seasonal Moving Average (SMA) Component**: Models the relationship between the current value and past error terms at the same seasonal lag, similar to the SMA component in SARIMA.\n",
    "\n",
    "4. **Exogenous (X) Variables**: These are external factors or regressors that you believe might influence the time series. Including exogenous variables can help the model capture additional patterns and relationships beyond the inherent time series components. These variables could be economic indicators, weather data, marketing campaigns, etc.\n",
    "\n",
    "SARIMAX models are represented as *SARIMAX(p, d, q) × (P, D, Q, s)*, where the $p$, $d$, $q$, $P$, $D$, $Q$, and $s$ parameters have the same meanings as in a regular SARIMA model.\n",
    "\n",
    "The equation for a SARIMAX(p, d, q) × (P, D, Q, s) model with exogenous variables can be complex, as it involves both the seasonal and non-seasonal components, as well as the influence of the exogenous variables. The equation includes terms to account for the relationships between the time series, its lagged values, seasonal terms, and the exogenous variables.\n",
    "\n",
    "Fitting a SARIMAX model with exogenous variables involves not only identifying the appropriate orders for the seasonal and non-seasonal components but also selecting and incorporating relevant exogenous variables. The estimation process usually involves statistical methods such as maximum likelihood estimation.\n",
    "\n",
    "**SARIMAX** models are powerful tools for time series forecasting, especially when you have access to additional external data that might impact the behavior of the time series you're analyzing. They provide a way to incorporate multiple sources of information into your forecasting process, potentially leading to more accurate and robust predictions.\n",
    "\n",
    "<div class=\"alert alert-info\"><h4>Related Functions:</h4>\n",
    "<tt><strong>\n",
    "<a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html'>sarimax.SARIMAX</a></strong><font color=green>(endog[, exog, order, …])</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<br>\n",
    "<strong><a href='https://www.statsmodels.org/stable/generated/statsmodels.tsa.vector_ar.var_model.VARResults.html'>sarimax.SARIMAXResults</a></strong><font color=green>(model, params, …[, …])</font>&nbsp;&nbsp;Class to hold results from fitting a SARIMAX model.</tt>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# Load specific forecasting tools\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf # for determining (p,q) orders\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose      # for ETS Plots\n",
    "from pmdarima import auto_arima     \n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales  Quantity\n",
       "Order Date                    \n",
       "2014-01-06  2573.820         9\n",
       "2014-01-07    76.728         3\n",
       "2014-01-10    51.940         1\n",
       "2014-01-11     9.940         2"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the sales data of Office Supplies\n",
    "df = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)\n",
    "df = df[df['Category']=='Furniture']\n",
    "df = df.groupby(by='Order Date').agg({'Sales':sum, 'Quantity':sum})\n",
    "df.sort_index(inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-12</th>\n",
       "      <td>2712.428</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-19</th>\n",
       "      <td>1250.473</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-26</th>\n",
       "      <td>1655.958</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-02</th>\n",
       "      <td>623.666</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-09</th>\n",
       "      <td>14.560</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales  Quantity\n",
       "Order Date                    \n",
       "2014-01-12  2712.428        15\n",
       "2014-01-19  1250.473        24\n",
       "2014-01-26  1655.958        26\n",
       "2014-02-02   623.666         5\n",
       "2014-02-09    14.560         2"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.resample('W').sum()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Sales'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Order Date'>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Quantity'].plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can treat `Quantity` as exogenous variable."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETS Decomposition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_sales = seasonal_decompose(df['Sales'])\n",
    "\n",
    "result_sales.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_sales = seasonal_decompose(df['Quantity'])\n",
    "\n",
    "result_sales.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test for stationarity\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic       -3.444275\n",
      "p-value                   0.009539\n",
      "# lags used               6.000000\n",
      "# observations          201.000000\n",
      "critical value (1%)      -3.463309\n",
      "critical value (5%)      -2.876029\n",
      "critical value (10%)     -2.574493\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df['Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic       -4.457632\n",
      "p-value                   0.000234\n",
      "# lags used               4.000000\n",
      "# observations          203.000000\n",
      "critical value (1%)      -3.462980\n",
      "critical value (5%)      -2.875885\n",
      "critical value (10%)     -2.574416\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df['Quantity'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tt>pmdarima.auto_arima</tt> to obtain recommended orders\n",
    "This may take awhile as there are a lot of combinations to evaluate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>208</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)</td> <th>  Log Likelihood     </th> <td>-1917.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 29 Aug 2023</td> <th>  AIC                </th> <td>3842.396</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>01:18:01</td>     <th>  BIC                </th> <td>3855.727</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-12-2014</td>    <th>  HQIC               </th> <td>3847.787</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 12-31-2017</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.7864</td> <td>    0.166</td> <td>    4.732</td> <td> 0.000</td> <td>    0.461</td> <td>    1.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -1.4946</td> <td>    0.201</td> <td>   -7.447</td> <td> 0.000</td> <td>   -1.888</td> <td>   -1.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>    0.5044</td> <td>    0.183</td> <td>    2.763</td> <td> 0.006</td> <td>    0.147</td> <td>    0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 6.442e+06</td> <td> 4.47e-08</td> <td> 1.44e+14</td> <td> 0.000</td> <td> 6.44e+06</td> <td> 6.44e+06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.12</td> <th>  Jarque-Bera (JB):  </th> <td>130.12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.73</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.43</td> <th>  Skew:              </th>  <td>1.32</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.14</td> <th>  Kurtosis:          </th>  <td>5.85</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 2.36e+30. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  208\n",
       "Model:               SARIMAX(1, 1, 2)   Log Likelihood               -1917.198\n",
       "Date:                Tue, 29 Aug 2023   AIC                           3842.396\n",
       "Time:                        01:18:01   BIC                           3855.727\n",
       "Sample:                    01-12-2014   HQIC                          3847.787\n",
       "                         - 12-31-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.7864      0.166      4.732      0.000       0.461       1.112\n",
       "ma.L1         -1.4946      0.201     -7.447      0.000      -1.888      -1.101\n",
       "ma.L2          0.5044      0.183      2.763      0.006       0.147       0.862\n",
       "sigma2      6.442e+06   4.47e-08   1.44e+14      0.000    6.44e+06    6.44e+06\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.12   Jarque-Bera (JB):               130.12\n",
       "Prob(Q):                              0.73   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.43   Skew:                             1.32\n",
       "Prob(H) (two-sided):                  0.14   Kurtosis:                         5.85\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 2.36e+30. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For SARIMA Orders we set seasonal=True and pass in an m value\n",
    "auto_arima(df['Sales'],bseasonal=True, m=12).summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df.iloc[:170]\n",
    "test = df.iloc[170:]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            4     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  9.57394D+00    |proj g|=  2.46033D-01\n",
      "\n",
      "At iterate    5    f=  9.25322D+00    |proj g|=  9.27008D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    4      8     14      1     0     0   7.396D-05   9.253D+00\n",
      "  F =   9.2531449377285586     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Sales</td>      <th>  No. Observations:  </th>    <td>170</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)</td> <th>  Log Likelihood     </th> <td>-1573.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 29 Aug 2023</td> <th>  AIC                </th> <td>3154.069</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>01:20:31</td>     <th>  BIC                </th> <td>3166.589</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-12-2014</td>    <th>  HQIC               </th> <td>3159.150</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 04-09-2017</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.7910</td> <td>    0.528</td> <td>   -1.499</td> <td> 0.134</td> <td>   -1.825</td> <td>    0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.2794</td> <td>    0.479</td> <td>   -0.583</td> <td> 0.560</td> <td>   -1.218</td> <td>    0.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.9240</td> <td>    0.525</td> <td>   -1.759</td> <td> 0.079</td> <td>   -1.953</td> <td>    0.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 6.972e+06</td> <td> 9.33e+05</td> <td>    7.472</td> <td> 0.000</td> <td> 5.14e+06</td> <td>  8.8e+06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>5.41</td> <th>  Jarque-Bera (JB):  </th> <td>70.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.02</td> <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.23</td> <th>  Skew:              </th> <td>1.14</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.44</td> <th>  Kurtosis:          </th> <td>5.21</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   No. Observations:                  170\n",
       "Model:               SARIMAX(1, 1, 2)   Log Likelihood               -1573.035\n",
       "Date:                Tue, 29 Aug 2023   AIC                           3154.069\n",
       "Time:                        01:20:31   BIC                           3166.589\n",
       "Sample:                    01-12-2014   HQIC                          3159.150\n",
       "                         - 04-09-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.7910      0.528     -1.499      0.134      -1.825       0.243\n",
       "ma.L1         -0.2794      0.479     -0.583      0.560      -1.218       0.660\n",
       "ma.L2         -0.9240      0.525     -1.759      0.079      -1.953       0.105\n",
       "sigma2      6.972e+06   9.33e+05      7.472      0.000    5.14e+06     8.8e+06\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   5.41   Jarque-Bera (JB):                70.94\n",
       "Prob(Q):                              0.02   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.23   Skew:                             1.14\n",
       "Prob(H) (two-sided):                  0.44   Kurtosis:                         5.21\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(train['Sales'],order=(1,1,2) ,enforce_invertibility=False)\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passing <tt>dynamic=False</tt> means that forecasts at each point are generated using the full history up to that point (all lagged values).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain predicted values\n",
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions = results.predict(start=start, end=end, dynamic=False).rename('SARIMA(1,0,0)(2,0,0,7) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, ''), Text(0, 0.5, 'Visitors per day')]"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "title='Restaurant Visitors'\n",
    "ylabel='Visitors per day'\n",
    "xlabel=''\n",
    "\n",
    "ax = test['Sales'].plot(legend=True,figsize=(12,6),title=title)\n",
    "predictions.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SARIMA(1,0,0)(2,0,0,7) MSE Error: 12077452.14\n",
      "SARIMA(1,0,0)(2,0,0,7) RMSE Error: 3475.262887\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tools.eval_measures import mse,rmse\n",
    "\n",
    "error1 = mse(test['Sales'], predictions)\n",
    "error2 = rmse(test['Sales'], predictions)\n",
    "\n",
    "print(f'SARIMA(1,0,0)(2,0,0,7) MSE Error: {error1:11.10}')\n",
    "print(f'SARIMA(1,0,0)(2,0,0,7) RMSE Error: {error2:11.10}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sales', 'Quantity'], dtype='object')"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            5     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  8.62475D+00    |proj g|=  1.90302D-02\n",
      "\n",
      "At iterate    5    f=  8.62437D+00    |proj g|=  4.27492D-03\n",
      "\n",
      "At iterate   10    f=  8.62420D+00    |proj g|=  8.86735D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    5     13     16      1     0     0   3.699D-05   8.624D+00\n",
      "  F =   8.6242022664645681     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>               <td>Sales</td>             <th>  No. Observations:  </th>    <td>170</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)x(2, 0, 0, 7)</td> <th>  Log Likelihood     </th> <td>-1466.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                  <td>Tue, 29 Aug 2023</td>        <th>  AIC                </th> <td>2942.229</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                      <td>01:22:51</td>            <th>  BIC                </th> <td>2957.908</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                   <td>01-12-2014</td>           <th>  HQIC               </th> <td>2948.591</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>- 04-09-2017</td>          <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>              <td>opg</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Quantity</th> <td>   97.6600</td> <td>    2.403</td> <td>   40.643</td> <td> 0.000</td> <td>   92.950</td> <td>  102.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>   -0.0783</td> <td>    0.072</td> <td>   -1.086</td> <td> 0.277</td> <td>   -0.220</td> <td>    0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L7</th>  <td>    0.2222</td> <td>    0.057</td> <td>    3.874</td> <td> 0.000</td> <td>    0.110</td> <td>    0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L14</th> <td>   -0.0828</td> <td>    0.094</td> <td>   -0.879</td> <td> 0.379</td> <td>   -0.267</td> <td>    0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 1.903e+06</td> <td> 1.58e+05</td> <td>   12.024</td> <td> 0.000</td> <td> 1.59e+06</td> <td> 2.21e+06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.02</td> <th>  Jarque-Bera (JB):  </th> <td>295.39</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.88</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.16</td> <th>  Skew:              </th>  <td>1.56</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.58</td> <th>  Kurtosis:          </th>  <td>8.66</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                     SARIMAX Results                                     \n",
       "=========================================================================================\n",
       "Dep. Variable:                             Sales   No. Observations:                  170\n",
       "Model:             SARIMAX(1, 0, 0)x(2, 0, 0, 7)   Log Likelihood               -1466.114\n",
       "Date:                           Tue, 29 Aug 2023   AIC                           2942.229\n",
       "Time:                                   01:22:51   BIC                           2957.908\n",
       "Sample:                               01-12-2014   HQIC                          2948.591\n",
       "                                    - 04-09-2017                                         \n",
       "Covariance Type:                             opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Quantity      97.6600      2.403     40.643      0.000      92.950     102.369\n",
       "ar.L1         -0.0783      0.072     -1.086      0.277      -0.220       0.063\n",
       "ar.S.L7        0.2222      0.057      3.874      0.000       0.110       0.335\n",
       "ar.S.L14      -0.0828      0.094     -0.879      0.379      -0.267       0.102\n",
       "sigma2      1.903e+06   1.58e+05     12.024      0.000    1.59e+06    2.21e+06\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):               295.39\n",
       "Prob(Q):                              0.88   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.16   Skew:                             1.56\n",
       "Prob(H) (two-sided):                  0.58   Kurtosis:                         8.66\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(train['Sales'],exog=train['Quantity'],order=(1,0,0),seasonal_order=(2,0,0,7),enforce_invertibility=False)\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain predicted values\n",
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "exog_forecast = test[['Quantity']]  # requires two brackets to yield a shape of (35,1)\n",
    "predictions = results.predict(start=start, end=end, exog=exog_forecast).rename('SARIMAX(1,0,0)(2,0,0,7) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, ''), Text(0, 0.5, 'Visitors per day')]"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "title='Restaurant Visitors'\n",
    "ylabel='Visitors per day'\n",
    "xlabel=''\n",
    "\n",
    "ax = test['Sales'].plot(legend=True,figsize=(12,6),title=title)\n",
    "predictions.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SARIMA(1,0,0)(2,0,0,7) MSE Error: 12077452.14\n",
      "SARIMA(1,0,0)(2,0,0,7) RMSE Error: 3475.262887\n",
      "\n",
      "SARIMAX(1,0,0)(2,0,0,7) MSE Error: 2317778.504\n",
      "SARIMAX(1,0,0)(2,0,0,7) RMSE Error: 1522.425205\n"
     ]
    }
   ],
   "source": [
    "# Print values from SARIMA above\n",
    "print(f'SARIMA(1,0,0)(2,0,0,7) MSE Error: {error1:11.10}')\n",
    "print(f'SARIMA(1,0,0)(2,0,0,7) RMSE Error: {error2:11.10}')\n",
    "print()\n",
    "\n",
    "error1x = mse(test['Sales'], predictions)\n",
    "error2x = rmse(test['Sales'], predictions)\n",
    "\n",
    "# Print new SARIMAX values\n",
    "print(f'SARIMAX(1,0,0)(2,0,0,7) MSE Error: {error1x:11.10}')\n",
    "print(f'SARIMAX(1,0,0)(2,0,0,7) RMSE Error: {error2x:11.10}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrain the model on the full data, and forecast the future\n",
    "We're going to forecast 39 days into the future, and use the additional holiday data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  8.90831D+00    |proj g|=  4.55523D-01\n",
      "\n",
      "At iterate    5    f=  8.77071D+00    |proj g|=  2.46477D-02\n",
      "\n",
      "At iterate   10    f=  8.76041D+00    |proj g|=  1.28819D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f=  8.75126D+00    |proj g|=  8.71626D-03\n",
      "\n",
      "At iterate   20    f=  8.75085D+00    |proj g|=  2.63679D-04\n",
      "\n",
      "At iterate   25    f=  8.75081D+00    |proj g|=  5.76846D-03\n",
      "\n",
      "At iterate   30    f=  8.74957D+00    |proj g|=  1.55926D-02\n",
      "\n",
      "At iterate   35    f=  8.74906D+00    |proj g|=  7.27861D-04\n",
      "\n",
      "At iterate   40    f=  8.74905D+00    |proj g|=  3.18560D-04\n",
      "\n",
      "At iterate   45    f=  8.74903D+00    |proj g|=  4.41136D-03\n",
      "\n",
      "At iterate   50    f=  8.74786D+00    |proj g|=  1.46329D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     55      1     0     0   1.463D-02   8.748D+00\n",
      "  F =   8.7478570300861893     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    }
   ],
   "source": [
    "model = SARIMAX(df['Sales'],exog=df['Quantity'],order=(1,2,1),seasonal_order=(1,2,2,7),enforce_invertibility=False)\n",
    "results = model.fit()\n",
    "exog_forecast = df[169:][['Quantity']]\n",
    "fcast = results.predict(len(df),len(df)+38,exog=exog_forecast).rename('SARIMAX(1,0,0)(2,0,0,7) Forecast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, ''), Text(0, 0.5, 'Visitors per day')]"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the forecast alongside historical values\n",
    "title='Restaurant Visitors'\n",
    "ylabel='Visitors per day'\n",
    "xlabel=''\n",
    "\n",
    "ax = df['Sales'].plot(legend=True,figsize=(16,6),title=title)\n",
    "fcast.plot(legend=True)\n",
    "ax.autoscale(axis='x',tight=True)\n",
    "ax.set(xlabel=xlabel, ylabel=ylabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}