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Forecasting overviewForecasting 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:
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.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.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.
ARIMA models
Use the following table to determine whether to use AI.FORECAST with the built-in TimesFM model or ML.FORECAST with an ARIMA_PLUS or ARIMA_PLUS_XREG model for your use case:
AI.FORECAST with a TimesFM model ML.FORECAST with an ARIMA_PLUS or ARIMA_PLUS_XREG model Model type Transformer-based foundation model. Statistical model that uses the ARIMA algorithm for the trend component, and a variety of other algorithms for non-trend components. For more information, see Time series modeling pipeline. Training required No, the TimesFM model is pre-trained. Yes, one ARIMA_PLUS or ARIMA_PLUS_XREG model is trained for each time series. SQL ease of use Very high. Requires a single function call. High. Requires a CREATE MODEL statement and a function call. Data history used Uses 512 time points. Uses all time points in the training data, but can be customized to use fewer time points. Accuracy Very high. Outperforms a number of other models. For more information, see A Decoder-only Foundation Model for Time-series Forecasting. Very high, on par with the TimesFM model. Customization Low. High. The CREATE MODEL statement offers arguments that let you tune many model settings, such as the following:
ARIMA_PLUS_XREG model. Explainability Low. High. You can use the ML.EXPLAIN_FORECAST function to inspect model components. Best use cases
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:
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-07 UTC.
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