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timesfm-forecasting

combined

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. Performs zero-shot time-series forecasting with Google's TimesFM, including regular-grid CSV preparation, quantile forecasts, XReg covariates, and held-out evaluation. Uses the Apache-licensed TimesFM 2.5 checkpoint by default and documents the distinct TimesFM 3.0 multivariate API and weight-license requirements.

When to use

• Forecasting **any univariate time series** (sales, demand, sensor, vitals, price, weather)
• You need **zero-shot forecasting** without training a custom model
• You want **probabilistic forecasts** with calibrated prediction intervals (quantiles)
• You have time series of **any length** (the model handles 1–16,384 context points)
• You need to **batch-forecast** hundreds or thousands of series efficiently
• You want a **foundation model** approach instead of hand-tuning ARIMA/ETS parameters
• You need classical statistical models with coefficient interpretation → use `statsmodels`
• You need time series classification or clustering → use `aeon`

When not to use

• classical statistical models with coefficient interpretation
• time series classification or clustering
• multivariate vector autoregression or Granger causality
• tabular non-temporal data

Skill page

Conflict

The K-Dense-AI copy is a materially different, newer procedure (version 3.0) that targets timesfm 3.0.2, distinguishes checkpoint 2.5 from 3.0, documents license restrictions for 3.0 weights, and adds workflow constraints (regular-grid validation, no dropna on internal gaps, rolling-origin evaluation, nominal interval labeling, MPS limitation, and a specific 2.5 quick-start with revision pinning). The other three copies are near-identical v1.0.0 reproductions of an older procedure that only covers TimesFM 2.5, uses a simpler install flow, and does not include the 3.0 API or license caveats. Crowning one v1.0.0 copy would hide the real alternative provided by the K-Dense-AI copy.