TimesFM Forecasting
技能 已验证 活跃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.
To provide agent users with a powerful and accessible tool for forecasting univariate time series data without the need for custom model training.
功能
- 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
使用场景
- 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
非目标
- 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.
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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