The TimesFM model

This document describes BigQuery ML's built-in TimesFM time series forecasting model.

The built-in TimesFM univariate model is an implementation of Google Research's open source TimesFM model. The Google Research TimesFM model is a foundation model for time-series forecasting that has been pre-trained on billions of time-points from many real-world datasets, so you can apply it to new forecasting datasets across many domains. The TimesFM model is available in all BigQuery supported regions.

Using BigQuery ML's built-in TimesFM model with the AI.FORECAST function lets you perform forecasting without having to create and train your own model, so you can avoid the need for model management. The forecast results from the TimesFM model are comparable to conventional statistical methods such as ARIMA. If you want more model tuning options than the TimesFM model offers, you can create an ARIMA_PLUS or ARIMA_PLUS_XREG model and use it with the ML.FORECAST function instead.

To try using a TimesFM model with the AI.FORECAST function, see Forecast multiple time series with a TimesFM univariate model.

To learn more about the Google Research TimesFM model, use the following resources:

• Google Research blog
• GitHub repository
• Hugging Face page