ai-ml-timeseries
Operational patterns, templates, and decision rules for time series forecasting (modern best practices): tree-based methods (LightGBM), deep learning (Transformers, RNNs), future-guided learning, temporal validation, feature engineering, generative TS (Chronos), and production deployment. Emphasizes explainability, long-term dependency handling, and adaptive forecasting.
npx skills add majiayu000/claude-skill-registry --skill ai-ml-timeseries --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Time Series Forecasting — Modern Patterns & Production Best Practices **Modern Best Practices (December 2025)**: - Treat **time** as a first-class axis: temporal splits, rolling backtests, and point-in-time correctness. - Default to **strong baselines** (naive/seasonal naive) before complex models. - Prevent leakage: feature windows and aggregations must use only information available at prediction time. - Evaluate by **horizon** and **segment**; a single aggregate metric hides failures. - Define retraining cadence and degraded modes (fallback model, last-known-good forecast). This skill provides **operational, copy-paste-ready workflows** for forecasting with recent advances: TS-specific EDA, temporal validation, lag/rolling features, model selection, multi-step forecasting, backtesting, generative AI (Chronos, TimesFM), and production deployment with drift monitoring. It focuses on **hands-on forecasting execution**, not theory. --- ## When to Use This Skill Claude should invoke this skill when the user asks for **hands-on time series forecasting**, e.g.: - "Build a time series model for X." - "Create lag features / rolling windows." - "Help design a forecasting backtest." - "P
- When to Use This Skill
- Quick Reference
- Decision Tree: Choosing Time Series Approach
- Core Concepts (Vendor-Agnostic)
- Implementation Practices (Tooling Examples)
- Do / Avoid
- Navigation: Core Patterns
- Time Series EDA & Data Preparation
- Feature Engineering
- Model Selection
- Forecasting Strategies
- Validation & Evaluation
- Generative & Advanced Models
- Production Deployment
What does the ai-ml-timeseries skill do?
Operational patterns, templates, and decision rules for time series forecasting (modern best practices): tree-based methods (LightGBM), deep learning (Transformers, RNNs), future-guided learning, temporal validation, feature engineering, generative TS (Chronos), and production deployment. Emphasizes explainability, long-term dependency handling, and adaptive forecasting.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-ml-timeseries --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.