Timesfm Forecasting
Google's TimesFM is a 200M parameter foundation model that does zero-shot forecasting on any univariate time series without training. Feed it sales, sensor data, weather readings, or vitals and get point forecasts plus calibrated prediction intervals (10th to 90th percentile). The skill includes a preflight checker that verifies you have the 4GB RAM and disk space before downloading the 800MB model, which matters because the agent won't crash your machine mid-run. Works CPU-only if needed, faster on GPU. Good for when you want probabilistic forecasts without tuning ARIMA parameters by hand or need to batch hundreds of series at once. Handles up to 16,384 context points per series.
- skills.sh installs
- 2.1K
- GitHub stars
- 56.6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add k-dense-ai/scientific-agent-skills --skill timesfm-forecasting --agent claude-codeInstalls into .claude/skills of the current project.
Files
TimesFM Forecasting
Choose the model/API first
The Python package version is 3.0.2; checkpoint versions are separate.
| Checkpoint | Interface | Quantile output | Usage terms |
|---|---|---|---|
| 2.5, 200M | timesfm.TimesFM_2p5_200M_torch, compile, forecast | mean + 9 deciles, median index 5 | Apache-2.0 weights; bundled CLI default |
| 3.0, about 330M | timesfm3.TimesFM3Forecaster, predict / predict_batch | 9 deciles, median index 4 | Downloaded weights restricted to non-commercial, non-production use |
Upstream identifies 3.0 as the latest model. This skill retains 2.5 as its default local workflow because its Apache-licensed weights have different usage terms. For 3.0 multivariate targets, past-only covariates or Apple MLX, read references/timesfm3.md before adapting code. Authorized Google Cloud services have separate terms; a Cloud entitlement does not change the license of downloaded weights. See the upstream license notice.
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- Repository
- github.com/k-dense-ai/scientific-agent-skills
- Categories
- AI & Agent Building, Automation & Workflows, Sales & Marketing
- First seen
- Jun 3, 2026
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