TimesFM Forecasting
技能 已验证 活跃Part of the AlterLab Academic Skills suite. 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 perform time series forecasting on univariate data without custom model training, leveraging a powerful foundation model.
功能
- Zero-shot time series forecasting
- Uses Google's TimesFM foundation model
- Supports CSV, DataFrame, and array inputs
- Provides point forecasts and prediction intervals
- Includes system requirements checker script
使用场景
- Forecasting sales, sensor data, or energy consumption
- Predicting stock prices or weather patterns
- Analyzing time series data without training custom models
- Generating probabilistic forecasts with confidence bands
非目标
- Classical statistical models requiring coefficient interpretation
- Time series classification or clustering
- Multivariate vector autoregression
- Tabular data processing (use scikit-learn instead)
Trust
- info:Issues AttentionThere are 2 open issues and 0 closed issues in the last 90 days, indicating low recent activity or a new/stable project.
安装
npx skills add AlterLab-IEU/AlterLab-Academic-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
TimesFM Forecasting
100Zero-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.
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