timesfm-forecasting

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

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

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 the examples/anomaly-detection/ directory for a full example.

⚠️ Mandatory Preflight: System Requirements Check

CRITICAL — ALWAYS run the system checker before loading the model for the first time.

This script checks:

1. Available RAM — warns if below 4 GB, blocks if below 2 GB
2. GPU availability — detects CUDA/MPS devices and VRAM
3. Disk space — verifies room for the ~800 MB model download
4. Python version — requires 3.10+
5. Existing installation — checks if timesfm and torch are installed

Note: Model weights are NOT stored in this repository. TimesFM weights (~800 MB) download on-demand from HuggingFace on first use and cache in ~/.cache/huggingface/. The preflight checker ensures sufficient resources before any download begins.