TimesFM Forecasting
Skill Verifiziert AktivZero-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.
To provide agent users with a powerful and accessible tool for forecasting univariate time series data without the need for custom model training.
Funktionen
- Zero-shot forecasting with TimesFM
- Supports univariate time series
- Outputs point forecasts and prediction intervals
- Includes a preflight system checker
- Handles CSV, DataFrame, and array inputs
Anwendungsfälle
- Forecasting sales, sensor data, or energy usage without custom models
- Obtaining probabilistic forecasts with calibrated prediction intervals
- Batch forecasting of multiple time series efficiently
- Leveraging foundation models for time series analysis
Nicht-Ziele
- Providing classical statistical models (ARIMA, ETS)
- Performing time series classification or clustering
- Implementing multivariate vector autoregression
- Offering built-in anomaly detection beyond quantile analysis
Execution
- info:Pinned dependenciesWhile package installation is covered, explicit dependency pinning via lockfiles isn't directly visible, though `uv` usage implies good management.
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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