End-to-end user journeys for time series forecasting models

This document describes the user journeys for BigQuery ML time series forecasting models, including the statements and functions that you can use to work with time series forecasting models. BigQuery ML offers the following types of time series forecasting models:

Model creation user journeys

The following table describes the statements and functions you can use to create time series forecasting models:

Model type

Model creation

Feature preprocessing

Hyperparameter tuning

Model weights

Tutorials

ARIMA_PLUS

CREATE MODEL

Automatic preprocessing

auto.ARIMA ¹ automatic tuning

ML.ARIMA_COEFFICIENTS

ARIMA_PLUS_XREG

CREATE MODEL

Automatic preprocessing

auto.ARIMA ¹ automatic tuning

ML.ARIMA_COEFFICIENTS

TimesFM

N/A

Forecast multiple time series

¹ The auto.ARIMA algorithm performs hyperparameter tuning for the trend module. Hyperparameter tuning isn't supported for the entire modeling pipeline. See the modeling pipeline for more details.

Model use user journeys

The following table describes the statements and functions you can use to evaluate, explain, and get forecasts from time series forecasting models:

Model type	Evaluation	Inference	AI explanation
`ARIMA_PLUS`	`ML.EVALUATE` ¹ `ML.ARIMA_EVALUATE` `ML.HOLIDAY_INFO`	`ML.FORECAST` `ML.DETECT_ANOMALIES`	`ML.EXPLAIN_FORECAST` ²
`ARIMA_PLUS_XREG`	`ML.EVALUATE` ¹ `ML.ARIMA_EVALUATE` `ML.HOLIDAY_INFO`	`ML.FORECAST` `ML.DETECT_ANOMALIES`	`ML.EXPLAIN_FORECAST` ²
TimesFM	N/A	`AI.FORECAST`	N/A

¹ You can input evaluation data to the ML.EVALUATE function to compute forecasting metrics such as mean absolute percentage error (MAPE). If you don't have evaluation data, you can use the ML.ARIMA_EVALUATE function to output information about the model like drift and variance.

² The ML.EXPLAIN_FORECAST function encompasses the ML.FORECAST function because its output is a superset of the results of ML.FORECAST .