A zero-shot time series forecaster built on Google's TimesFM foundation model. Feed it any univariate series (sales, sensor data, stock prices) and get point forecasts plus calibrated prediction intervals without training anything. The 200M parameter model needs about 4GB RAM and includes a mandatory preflight checker to verify your machine can handle it before loading. Supports basic forecasting and covariate-based forecasting (XReg) with exogenous variables. The honest take: this is genuinely useful if you need quick forecasts across many different series without tuning ARIMA parameters for each one, but you're still loading an 800MB model into memory. Works with context lengths up to 16,384 points and handles batch forecasting efficiently.
npx skills add https://github.com/google-research/timesfm --skill timesfm-forecasting