Overview
Since introducing zero-shot time-series forecasting in BigQuery with AI.FORECAST — initially powered by TimesFM 2.0 and later upgraded to TimesFM 2.5 — you have been able to generate forecasts directly in SQL without training or managing custom models. However, up until TimesFM 2.5, these foundation models were limited to univariate forecasting, predicting future values from the history of a single time series alone. In practice, most real-world forecasting problems are multivariate, where related targets and external drivers like weather, promotions, and holidays jointly shape future outcomes.
Today, we are thrilled to announce the preview of TimesFM 3.0 in BigQuery. Developed by Google Research, TimesFM 3.0 is a 330-million parameter state-of-the-art foundation model designed to unlock native multivariate time-series forecasting directly inside BigQuery SQL. TimesFM 3.0 ranks #1 across all three major public time-series leaderboards — GIFT-Eval, FEV-Bench, and TIME — achieving top zero-shot accuracy on both covariate and multivariate forecasting tasks compared to existing foundation models.
Alongside its new multivariate capabilities, TimesFM 3.0 also serves as a drop-in upgrade for univariate forecasting via AI.FORECAST and anomaly detection via AI.DETECT_ANOMALIES. Existing workflows can immediately benefit from higher benchmark accuracy simply by specifying model => ‘TimesFM 3.0’.
What’s new: Multivariate foundation modeling
With TimesFM 3.0, BigQuery brings enterprise-grade multivariate modeling into familiar SQL workflows via AI.FORECAST:
|
Capability |
TimesFM 2.0 / 2.5 |
TimesFM 3.0 |
|
Forecasting scope |
Univariate only |
Univariate and multivariate |
|
Target columns |
Single target |
Single or multiple targets |
|
Covariate columns with past-only values |
Not supported |
Supported |
|
Covariate columns with past and future values |
Not supported |
Supported |
Multivariate forecasting with BigQuery SQL
To see how these capabilities work together, let’s forecast daily commuter (subscriber_trips) and leisure (casual_trips) bike rides over a seven-day horizon using the NYC Citi Bike public dataset (bigquery-public-data.new_york.citibike_trips).
BigQuery uses a single-table input format for multivariate forecasting that combines two sets of rows:
-
Historical observation rows: timestamps between start time (2016-01-01) and the forecast cutoff date (2016-06-01), fully populated with target metrics, past covariates, and known future covariates
-
Future horizon rows: timestamps across the seven-day forecast horizon (2016-06-01 to 2016-06-07) populated with known future covariates, while target columns and past-only covariates are set to NULL
