Google AI Releases TimesFM-3: A 330M Parameter Zero-Shot Foundation Model For Multivariate Time Series Forecasting

Google Research released TimesFM-3, a 330 million parameter time series foundation model that performs zero-shot multivariate forecasting in a single forward pass, accepting multiple targets and covariates without fine-tuning. It achieves the top average rank among pretrained foundation models on GIFT-Eval, fev-bench, and the TIME leaderboard, though its weights are released under a non-commercial, non-production license.

Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM checkpoint through 2.5, it is pretrained natively for multivariate forecasting, accepting 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. The weights, however, ship under a non-commercial, non-production license. The post Google AI Releases TimesFM-3: A 330M Parameter Zero-Shot Foundation Model For Multivariate Time Series Forecasting https://www.marktechpost.com/2026/08/31/google-ai-releases-timesfm-3-a-330m-parameter-zero-shot-foundation-model-for-multivariate-time-series-forecasting/ appeared first on MarkTechPost https://www.marktechpost.com .