timesfm-forecasting
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
This page mirrors an upstream repository entry. It does not mean the skill is already part of the SCI Skills curated catalog.
- Raw path
- scientific-skills/timesfm-forecasting
- Allowed tools
- Read, Write, Edit, Bash
- Repository version
- 2.31.0
- Synced at
- March 27, 2026
About this skill
About this skill
TimesFM (Time Series Foundation Model) is a pretrained decoder-only foundation model developed by Google Research for time-series forecasting. It works **zero-shot** — feed it any univariate time series and it returns point forecasts with calibrated quantile prediction intervals, no training required.
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