TimesFM
Forecasts regularly sampled time series without training a new model for each dataset.
- Field
- Cross-domain
- Method
- Foundation model
- Data it takes
- time-series
- License
- Apache-2.0
TimesFM forecasts a time series it has not been trained on. It is a decoder-only model handling context windows up to 16,000 points, and can return prediction intervals rather than a single trajectory.
No training run or per-series fitting is required. It suits streamflow and climate reanalysis series, and gap-filling in eddy covariance and soil moisture records. Where sufficient history exists to fit a dedicated model, a trained approach such as NeuralHydrology generally performs better.
Catalog entry last checked 2026-08-17. All models →
Run TimesFM on your data
The lab runs this on its own compute, in a container, with the inputs and parameters recorded alongside the result. Initial scoping conversations are free.
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