Data Science & Analytics
TimesFM Forecasting
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.
- Publisher / source
- google-research/timesfm (found on GitHub top repositories)
- Upstream record
- google-research/timesfm
- Version
- Version 1
- Published
- Publication review
- Approved for publication
- Content fingerprint
cb83b741010d9f14448ca41866bcdf6f66bbaca28f5c3146b39663a1d86c6f18