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 forec
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- 20.09.2026 г.
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TimesFM Forecasting
Overview
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.
This skill includes a mandatory preflight system checker that verifies RAM, GPU memory, and disk space before the model is ever loaded so the agent never crashes the user's machine.
Key numbers: TimesFM 2.5 uses 200M parameters (~800 MB on disk, ~1.5 GB in RAM on CPU, ~1 GB VRAM on GPU). The archived v1/v2 500M-parameter model needs ~32 GB RAM. Always run the system checker first.
When to Use This Skill
Use this skill when:
- 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 covariate forecasting with exogenous variables (price, promotions, holidays, day-of-week effects) → use
forecast_with_covariates()(TimesFM 2.5 +pip install timesfm[xreg])
Do not use this skill when:
- You need classical statistical models with coefficient interpretation → use
statsmodels - You need time series classification or clustering → use
aeon - You need multivariate vector autoregression or Granger causality → use
statsmodels - Your data is tabular (not temporal) → use
scikit-learn - You cannot install optional dependencies → XReg requires scikit-learn and JAX
Note on Anomaly Detection: TimesFM does not have built-in anomaly detection, but you can use the quantile forecasts as prediction intervals — values outside the 90% CI (q10–q90) are statistically unusual. See
examples/anomaly-detection/for a full example.
⚠️ Mandatory Preflight: System Requirements Check
CRITICAL — ALWAYS run the system checker before loading the model for the first time.
python scripts/check_system.py
This script checks: