TimesFM Forecasting
Forecasts univariate time series zero-shot with Google's TimesFM, with optional covariates.
Install
npx skills add google-research/timesfm --skill timesfm-forecasting -g -a claude-code -y- Repository
- google-research/timesfm35k
- Category
- Data and ML
- Updated
- 1mo ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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
