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:

• ai-ml-data-science – General data science workflows, EDA, feature engineering, evaluation
• ai-mlops – Model deployment, monitoring, drift detection, retraining automation

If the user is asking about LLM/RAG/search, prefer:

• ai-llm – LLM fine-tuning, prompting, evaluation
• ai-rag – RAG pipeline design and optimization

Quick Reference

Decision Tree: Choosing Time Series Approach

User needs time series forecasting for: [Data Type]
    ├─ Strong Seasonality?
    │   ├─ Simple patterns? → LightGBM with seasonal features
    │   ├─ Complex patterns? → LightGBM + Prophet comparison
    │   └─ Multiple seasonalities? → Prophet or TBATS
    │
    ├─ Long-term Dependencies (>50 steps)?
    │   ├─ Transformers (TimesFM, Chronos) → Best for complex patterns
    │   └─ RNNs/LSTMs → Good for sequential dependencies
    │
    ├─ Event Forecasting (binary outcomes)?
    │   └─ Temporal classification / survival modelling → validate with time-based splits
    │
    ├─ Intermittent/Sparse Data (many zeros)?
    │   ├─ Croston/SBA → Classical intermittent methods
    │   └─ LightGBM with zero-inflation features → Modern approach
    │
    ├─ Multiple Covariates?
    │   ├─ LightGBM → Best with many features
    │   └─ TFT/DeepAR → If deep learning needed
    │
    └─ Explainability Required (healthcare, finance)?
        ├─ LightGBM → SHAP values, feature importance
        └─ Linear models → Most interpretable

Core Concepts (Vendor-Agnostic)

• Time axis: splits, features, and labels must respect time ordering and availability.
• Non-stationarity: seasonality, trend, and regime shifts are normal; monitor and retrain intentionally.
• Evaluation: rolling/expanding backtests; report horizon-wise and segment-wise performance.
• Operationalization: define retraining cadence, fallback models, and data freshness contracts.
• Data governance: treat time series as potentially sensitive; enforce access control, retention, and PII scrubbing in logs.

Implementation Practices (Tooling Examples)

• Build features with explicit time windows; store cutoff timestamps with each training run.
• Backtest with a standardized harness (rolling/expanding windows, horizon-wise metrics).
• Log production forecasts with metadata (model version, horizon, data cut) to enable debugging.
• Implement fallbacks (baseline model, last-known-good, “insufficient data” handling) for outages and anomalies.

Do / Avoid

Do

• Do start with naive/seasonal naive baselines and compare against learned models (Forecasting: Principles and Practice: https://otexts.com/fpp3/).
• Do backtest with rolling windows and preserve point-in-time correctness.
• Do monitor for data pipeline changes (missing timestamps, level shifts, calendar changes).
• Do align metrics/loss to the decision: asymmetric costs, service levels, and probabilistic targets (quantiles/intervals) when needed.

Avoid

• Avoid random splits for forecasting problems.
• Avoid features that use future information (future aggregates, leakage via target encoding).
• Avoid optimizing only aggregate metrics; always inspect horizon-wise errors and worst segments.
• Avoid MAPE when the target can be 0 or near-0; prefer MASE/WAPE/sMAPE and horizon-wise reporting.

Navigation: Core Patterns

Time Series EDA & Data Preparation

• TS EDA Best Practices
  • Frequency detection, missing timestamps, decomposition
  • Outlier detection, level shifts, seasonality analysis
  • Granularity selection and stability checks

Feature Engineering

• Lag & Rolling Patterns
  • Lag features (lag_1, lag_7, lag_28 for daily data)
  • Rolling windows (mean, std, min, max, EWM)
  • Avoiding leakage, seasonal lags, datetime features

Model Selection

• Model Selection Guide

  • Decision rules: Strong seasonality → LightGBM, Long-term → Transformers
  • Benchmark comparison: LightGBM vs Prophet vs Transformers vs RNNs
  • Explainability considerations for mission-critical domains

• LightGBM TS Patterns (feature-based forecasting best practices)

  • Why LightGBM excels: performance + efficiency + explainability
  • Feature engineering for tree-based models
  • Hyperparameter tuning for time series

Forecasting Strategies

• Multi-Step Forecasting Patterns

  • Direct strategy (separate models per horizon)
  • Recursive strategy (feed predictions back)
  • Seq2Seq strategy (Transformers, RNNs for long horizons)

• Intermittent Demand Patterns

  • Croston, SBA, ADIDA for sparse data
  • LightGBM with zero-inflation features (modern approach)
  • Two-stage hurdle models, hierarchical Bayesian

Validation & Evaluation

• Backtesting Patterns
  • Rolling window backtest, expanding window
  • Temporal train/validation split (no IID splits!)
  • Horizon-wise metrics, segment-level evaluation

Generative & Advanced Models

• TS-LLM Patterns
  • Chronos, TimesFM, Lag-Llama (Transformer models)
  • Event forecasting patterns (temporal classification, survival modelling)
  • Tokenization, discretization, trajectory sampling

Production Deployment

• Production Deployment Patterns
  • Feature pipelines (same code for train/serve)
  • Retraining strategies (time-based, drift-triggered)
  • Monitoring (error drift, feature drift, volume drift)
  • Fallback strategies, streaming ingestion, data governance

Navigation: Templates (Copy-Paste Ready)

Data Preparation

• TS EDA Template – Reproducible structure for time series analysis
• Resample & Fill Template – Handle missing timestamps and resampling

Feature Templates

• Lag & Rolling Features – Create temporal features for ML models
• Calendar Features – Business calendars, holidays, events

Model Templates

• Forecast Model Template – End-to-end forecasting pipeline (LightGBM, transformers, RNNs)
• Multi-Step Strategy – Direct, recursive, and seq2seq approaches

Evaluation Templates

• Backtest Template – Rolling window validation setup
• TS Metrics Template – MAPE, MAE, RMSE, MASE, pinball loss

Advanced Templates

• TS-LLM Template – Time series foundation model patterns and experimental approaches

Related Skills

For adjacent topics, reference these skills:

• ai-ml-data-science – EDA workflows, feature engineering patterns, model evaluation, SQLMesh transformations
• ai-mlops – Production deployment, monitoring, retraining pipelines
• ai-llm – Fine-tuning approaches applicable to time series LLMs (Chronos, TimesFM)
• ai-prompt-engineering – Prompt design patterns for time series LLMs
• data-sql-optimization – SQL optimization for time series data storage and retrieval

External Resources

See data/sources.json for curated web resources including:

• Classical methods (statsmodels, Prophet, ARIMA)
• Deep learning frameworks (PyTorch Forecasting, GluonTS, Darts, NeuralProphet)
• Transformer models (TimesFM, Chronos, Lag-Llama, Informer, Autoformer)
• Anomaly detection tools (PyOD, STUMPY, Isolation Forest)
• Feature engineering libraries (tsfresh, TSFuse, Featuretools)
• Production deployment (Kats, MLflow, sktime)
• Benchmarks and datasets (M5 Competition, Monash Time Series, UCI)

Usage Notes

For Claude:

• Activate this skill for hands-on forecasting tasks, feature engineering, backtesting, or production setup
• Start with Quick Reference and Decision Tree for fast guidance
• Drill into references/ for detailed implementation patterns
• Use assets/ for copy-paste ready code
• Always check for temporal leakage (future data in training)
• Start with strong baselines; choose model family based on horizon, covariates, and latency/cost constraints
• Emphasize explainability for healthcare/finance domains
• Monitor for data distribution shifts in production

Key Principle: Time series forecasting is about temporal structure, not IID assumptions. Use temporal validation, avoid future leakage, and choose models based on horizon length and data characteristics.