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About timesfm-forecasting
timesfm-forecasting
Performs zero-shot time-series forecasting
timesfm-forecasting is an agent skill for zero-shot time-series forecasting with Google's TimesFM. It covers preparing regular-grid CSV data, producing quantile forecasts, using XReg covariates, and held-out evaluation. The skill begins by making you choose the model and its interface, because the Python package version and the model checkpoint version are separate. For TimesFM 2.5 with 200 million parameters, the interface gives a mean and nine deciles, and its weights are Apache-2.0. The newer TimesFM 3.0, with about 330 million parameters, gives nine deciles and has downloaded weights restricted to non-commercial, non-production use. The skill keeps 2.5 as its default local workflow because its Apache-licensed weights have different usage terms. For 3.0 work on multivariate targets, past-only covariates or Apple MLX, it points to a separate reference file to read before adapting code. A Google Cloud entitlement does not change the licence of downloaded weights, so check the upstream licence notice before commercial use. Use it when you need a forecast for a regular time series without training a model on your own data.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/timesfm-forecasting