Forecasting overview

Forecasting is a technique where you analyze historical data in order to make an informed prediction about future trends. For example, you might analyze historical sales data from several store locations in order to predict future sales at those locations. In BigQuery ML, you perform forecasting on time series data.

You can perform forecasting in the following ways:

  • By using the AI.FORECAST function with the built-in TimesFM model. Use this approach when you need to forecast future values for a single variable, and don't require the ability to fine-tune the model. This approach doesn't require you to create and manage a model.
  • By using the ML.FORECAST function with the ARIMA_PLUS model. Use this approach when you need to run an ARIMA-based modeling pipeline and decompose the time series into multiple components in order to explain the results. This approach requires you to create and manage a model.
  • By using the ML.FORECAST function with the ARIMA_PLUS_XREG model. Use this approach when you need to forecast future values for multiple variables. This approach requires you to create and manage a model.

ARIMA_PLUS and ARIMA_PLUS_XREG time series models aren't actually single models, but rather a time series modeling pipeline that includes multiple models and algorithms. For more information, see Time series modeling pipeline.

By using the default settings of BigQuery ML's statements and functions, you can create and use a forecasting model even without much ML knowledge. However, having basic knowledge about ML development, and forecasting models in particular, helps you optimize both your data and your model to deliver better results. We recommend using the following resources to develop familiarity with ML techniques and processes: