BigQuery TimesFM 3.0 with multivariate time-series forecasting
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 3.0is 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...
Copyright of this story solely belongs to cloud.google.com. To see the full text click HERE
