Agent skill · data analytics · k-dense-ai
timesfm-forecasting
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.
Why this skill is useful
Adds a preflight system checker script that ensures sufficient resources before model loading, which is crucial for successful execution.
What it needs
Requires torch installed locally. About 8k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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 wraps TimesFM for safe, agent-friendly local inference. It 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 a 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 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/timesfm-forecasting