Time-Series-Analysis-and-Forecasting-with-Python

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.

Time series are widely used for non-stationary data, like economics, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series. Let’s get started!

Contents

• Datasets Info

• Introduction to Time Series Analysis(Theory)

  • Taxonomy of Time Series Analysis Domain
  • Best Practices for Forecasting Model Selection
  • Simple and Classical Forecasting Methods
  • Time Series to Supervised Learning Problem
  • Deep Learning for Time Series Forecasting

• Time Series Data Visualization

  • Plotting of Pandas Df
  • Adding title
  • Adding Axis label
  • X limits by slice
  • X limit by argument
  • Color and Style
  • X ticks spacing
  • Date formatting
  • Major and Minor axis values
  • Gridlines

• Time Series EDA

  • Introduction with time series data
  • Time resampling
  • Time downsampling/upsampling
  • Time Shifting
  • forward shift
  • backward shift
  • Rolling window mean
  • Expanding window mean/cumulative mean

• Time Series Data Analysis

  • Introduction to statsmodels
  • Hodrick Prescott filter - Trend/cyclical components
  • Time Series Stationarity
  • Augmented Dickey-Fuller Test
  • Granger Causality Tests
  • Time series decomposition
  • Additive/multiplicative models
  • Moving Average
  • Simple Exponentially weighted moving average(EWMA)
  • Double EWMA
  • Holt-Winters Method(Triple EWMA)

• Time Series Forecasting Classical Methods

  • Forecasting with Holts-Winter Method
  • Autocorrelation function(ACF)
  • Partial autocorrelation function(PACF)
  • Autocovariance for 1D
  • Autocorrelation for 1D
  • Autoregressive model(AR(p))
  • Autoregressive Moving Average(ARMA) Model
  • Autoregressive Integrated Moving Average(ARIMA)
  • Error/Trend/Seasonal Decomposition(ETS Decomposition)
  • Seasonal Autoregressive Integrated Moving Averages(SARIMA)
  • Seasonal AutoRegressive Integrated Moving Average with EXogenous Variable.

• Time Series Forecasting with LSTMs

  • MLPs for time series forecasting(under construction)
  • LSTMs for time series forecasting
  • CNNs for time series forecasting(under construction)
  • Transformers for time series forecasting(under construction)