Data Science & Analytics
AlterLab TimesFM
Forecasts time series zero-shot with Google's TimesFM foundation models — TimesFM 2.5 (200M, Apache-2.0 weights; ForecastConfig API, XReg covariates) and TimesFM 3.0 (~330M, multivariate with native past/future covariates; non-commercial weights) — producing point forecasts and quantile prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use when forecasting univariate or multivariate series (sales, sensors, energy, vitals, weather) without training a custom model, batch-forecasting many series, or flagging anomalies against forecast intervals. Part of the AlterLab Academic Skills suite.
- Publisher / source
- AlterLab-IEU/AlterLab-Academic-Skills (found on the CSV list)
- Upstream record
- AlterLab-IEU/AlterLab-Academic-Skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
454c1e6c654d2f445de908d81f51a2dc54d7c7c902306a73494852f34deeb98f