Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Every TimesFM checkpoint through 2.5 was univariate: one series, its own history, nothing else. TimesFM-3 is pretrained natively for multivariate forecasting on more than 1 trillion time points, and accepts multiple targets, past covariates, and past-future covariates with no task-specific fine-tuning. It takes the top average rank among pretrained foundation models on GIFT-Eval, fev-bench, and the TIME leaderboard, on both point and probabilistic metrics.
Is it deployable?
Partial, the TimesFM repository code is Apache-2.0, but the TimesFM 3.0 weights ship under timesfm-non-commercial-license-v1.0. They are restricted to non-commercial, non-production use. You can benchmark it today. You cannot ship it behind a production forecast API.
What changed
Every TimesFM release through 2.5 was univariate. It forecast one series from its own history. Most real forecasting problems are not shaped that way. Google’s example is ice cream sales, where related product sales, foot traffic, weather, promotions, and holidays all move the target.
