timesfm-forecastinglisted

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.
JZKK720/cubecloud-skills-bundle-kit · ★ 3 · AI & Automation · score 69

Install: claude install-skill JZKK720/cubecloud-skills-bundle-kit

# 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 thi