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create_single_timeseries_forecasting_model_test.py
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (t
# you may not use this file except in compliance wi
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in
# distributed under the License is distributed on a
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, eit
# See the License for the specific language governi
# limitations under the License.
def test_create_single_timeseries() -> None:
# [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial]
import bigframes.pandas as bpd
# Start by loading the historical data from BigQuerythat you want to analyze and forecast.
# This clause indicates that you are querying the ga_sessions_* tables in the google_analytics_sample dataset.
# Read and visualize the time series you want to forecast.
df = bpd.read_gbq("bigquery-public-data.google_analytics_sample.ga_sessions_*")
parsed_date = bpd.to_datetime(df.date, format="%Y%m%d", utc=True)
parsed_date.name = "parsed_date"
visits = df["totals"].struct.field("visits")
visits.name = "total_visits"
total_visits = visits.groupby(parsed_date).sum()
# Expected output: total_visits.head()
# parsed_date
# 2016-08-01 00:00:00+00:00 1711
# 2016-08-02 00:00:00+00:00 2140
# 2016-08-03 00:00:00+00:00 2890
# 2016-08-04 00:00:00+00:00 3161
# 2016-08-05 00:00:00+00:00 2702
# Name: total_visits, dtype: Int64
total_visits.plot.line()
# [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial]
# [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial_create]
from bigframes.ml import forecasting
import bigframes.pandas as bpd
# Create a time series model to forecast total site visits:
# The auto_arima option defaults to True, so the auto.ARIMA algorithm automatically
# tunes the hyperparameters in the model.
# The data_frequency option defaults to 'auto_frequency so the training
# process automatically infers the data frequency of the input time series.
# The decompose_time_series option defaults to True, so that information about
# the time series data is returned when you evaluate the model in the next step.
model = forecasting.ARIMAPlus()
model.auto_arima = True
model.data_frequency = "auto_frequency"
model.decompose_time_series = True
# Use the data loaded in the previous step to fit the model
training_data = total_visits.to_frame().reset_index(drop=False)
X = training_data[["parsed_date"]]
y = training_data[["total_visits"]]
model.fit(X, y)
# [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial_create]
# [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial_coef]
coef = model.coef_
print(coef.peek())
# Expected output:
# ar_coefficients ma_coefficients intercept_or_drift
# 0 [0.40944762] [-0.81168198] 0.0
# [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial_coef]
# [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial_evaluate]
# Evaluate the time series models by using the summary() function. The summary()
# function shows you the evaluation metrics of all the candidate models evaluated
# during the process of automatic hyperparameter tuning.
summary = model.summary(
show_all_candidate_models=True,
)
print(summary.peek())
# Expected output:
# row non_seasonal_p non_seasonal_d non_seasonal_q has_drift log_likelihood AIC variance seasonal_periods has_holiday_effect has_spikes_and_dips has_step_changes error_message
# 0 0 1 3 True -2464.255656 4938.511313 42772.506055 ['WEEKLY'] False False True
# 1 2 1 0 False -2473.141651 4952.283303 44942.416463 ['WEEKLY'] False False True
# 2 1 1 0 False -2479.880885 4963.76177 46642.953433 ['WEEKLY'] False False True
# 3 0 1 1 False -2470.632377 4945.264753 44319.379307 ['WEEKLY'] False False True
# 4 2 1 1 True -2463.671247 4937.342493 42633.299513 ['WEEKLY'] False False True
# [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial_evaluate]
# [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial_forecast]
prediction = model.predict(horizon=30, confidence_level=0.8)
print(prediction.peek())
# Expected output:
# forecast_timestamp forecast_value standard_error confidence_level prediction_interval_lower_bound prediction_interval_upper_bound confidence_interval_lower_bound confidence_interval_upper_bound
# 11 2017-08-13 00:00:00+00:00 1845.439732 328.060405 0.8 1424.772257 2266.107208 1424.772257 2266.107208
# 29 2017-08-31 00:00:00+00:00 2615.993932 431.286628 0.8 2062.960849 3169.027015 2062.960849 3169.027015
# 7 2017-08-09 00:00:00+00:00 2639.285993 300.301186 0.8 2254.213792 3024.358193 2254.213792 3024.358193
# 25 2017-08-27 00:00:00+00:00 1853.735689 410.596551 0.8 1327.233216 2380.238162 1327.233216 2380.238162
# 1 2017-08-03 00:00:00+00:00 2621.33159 241.093355 0.8 2312.180802 2930.482379 2312.180802 2930.482379
# [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial_forecast]
# [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial_explain_forecast]
ex_pred = model.predict_explain(horizon=30, confidence_level=0.8)
print(ex_pred.head(4))
# Expected output:
# time_series_timestamp time_series_type time_series_data time_series_adjusted_data standard_error confidence_level prediction_interval_lower_bound prediction_interval_upper_bound trend seasonal_period_yearly seasonal_period_quarterly seasonal_period_monthly seasonal_period_weekly seasonal_period_daily holiday_effect spikes_and_dips step_changes residual
# 0 2016-08-01 00:00:00+00:00 history 1711.0 505.716474 206.939556 <NA> <NA> <NA> 0.0 <NA> <NA> <NA> 169.611938 <NA> <NA> <NA> 1205.283526 336.104536
# 1 2016-08-02 00:00:00+00:00 history 2140.0 623.137701 206.939556 <NA> <NA> <NA> 336.104428 <NA> <NA> <NA> 287.033273 <NA> <NA> <NA> 1205.283526 311.578773
# 2 2016-08-03 00:00:00+00:00 history 2890.0 1008.655091 206.939556 <NA> <NA> <NA> 563.514213 <NA> <NA> <NA> 445.140878 <NA> <NA> <NA> 1205.283526 676.061383
# 3 2016-08-04 00:00:00+00:00 history 3161.0 1389.40959 206.939556 <NA> <NA> <NA> 986.317236 <NA> <NA> <NA> 403.092354 <NA> <NA> <NA> 1205.283526 566.306884
# 4 2016-08-05 00:00:00+00:00 history 2702.0 1394.395741 206.939556 <NA> <NA> <NA> 1248.707386 <NA> <NA> <NA> 145.688355 <NA> <NA> <NA> 1205.283526 102.320733
# 5 2016-08-06 00:00:00+00:00 history 1663.0 437.09243 206.939556 <NA> <NA> <NA> 1188.59004 <NA> <NA> <NA> -751.49761 <NA> <NA> <NA> 1205.283526 20.624044
# [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial_explain_forecast]
assert coef is not None
assert ex_pred is not None
assert summary is not None
assert model is not None
assert parsed_date is not None
assert prediction is not None
assert total_visits is not None