BigQuery offers TimesFM 3.0, a built-in time series forecasting model that implements Google Research's open

## Feature Feature BigQuery offers [TimesFM 3.0](https://research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/), a built-in time series forecasting model that implements Google Research's open source TimesFM model. You can use `TimesFM 3.0` with the following functions: * Use[AI.FORECAST](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast)to perform time series forecasting. In addition to univariate forecasting, you can now perform multivariate forecasting that predicts values for multiple time series based on historical values and additional covariates. * Use[AI.EVALUATE](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-evaluate)to perform univariate time series evaluation. * Use[AI.DETECT\_ANOMALIES](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-detect-anomalies)to perform univariate time series anomaly detection. To try using the `TimesFM 3.0` model for multivariate forecasting with the`AI.FORECAST` function, see[Forecast a single time series with a TimesFM multivariate model](https://cloud.google.com/bigquery/docs/timesfm-multivariate-single-time-series-forecasting-tutorial)and[Forecast multiple time series with a TimesFM multivariate model](https://cloud.google.com/bigquery/docs/timesfm-multivariate-multi-time-series-forecasting-tutorial). The `TimesFM 3.0` model is in [Preview](https://cloud.google.com/products/#product-launch-stages).