TimesFM Forecasting
Skill Verified ActivePart 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.
Features
- 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
Use Cases
- 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
Non-Goals
- 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.
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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