ai-ml-timeseries

Time Series Forecasting — Modern Patterns & Production Best Practices

Modern Best Practices (January 2026):

• 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.
• Prefer probabilistic forecasts when decisions are risk-sensitive (quantiles/intervals); evaluate calibration (coverage) and use pinball/CRPS.
• For many related series, consider global + hierarchical approaches (shared models + reconciliation); validate across levels and key segments.
• Treat time zones/DST as first-class; validate timestamp alignment before feature generation.
• 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."
• "Pick the right forecasting model for my data."
• "Fix leakage in forecasting."
• "Evaluate multi-horizon forecasts."
• "Use LLMs or generative models for TS."
• "Set up monitoring for a forecast system."
• "Implement LightGBM for time series."
• "Use transformer models (TimesFM, Chronos) for forecasting."
• "Apply temporal classification/survival modelling for event prediction."

If the user is asking about general ML modelling, deployment, or infrastructure, prefer: