Google introduced TimesFM-3, a 330-million-parameter time series model that forecasts multiple related data streams simultaneously using a single forward pass. It outperforms competing foundation models on three major benchmarks by incorporating both historical and future-known information (like promotions or weather) to improve prediction accuracy.

• TimesFM-3 handles multivariate forecasting natively—predicting multiple related time series at once while capturing dependencies between them, unlike previous versions limited to single series.
• The model generates entire forecast horizons in one pass using "contiguous patch masking" instead of iterative generation, reducing latency and error accumulation.
• It supports three data types: multiple targets, past-only features, and known-future covariates (like scheduled promotions), letting it incorporate planned events that univariate models miss entirely.
• On Gift-Eval, FEV-Bench, and Time benchmarks, TimesFM-3 ranked first among foundation models for both point and probabilistic forecasting accuracy.