Google's TimesFM 3.0 Brings Zero-Shot Time-Series Forecasting with Multivariate Support
Google's TimesFM 3.0 is a pretrained foundation model for time-series forecasting that enables zero-shot predictions without training a separate model for each dataset. Version 3.0 introduces native multivariate forecasting and covariate support, making it more suited to real-world datasets. The model returns both point forecasts and quantile estimates, allowing teams to assess uncertainty and plan around different risk levels. It is available via PyPI with PyTorch support, though the pretrained weights currently carry a non-commercial license, separate from the Apache-2.0 source code license. Developers are advised to verify licensing terms before considering any production or commercial deployment.
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