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name: ai-ml-timeseries description: > 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.

Time Series Forecasting — Modern Patterns & Production Best Practices

Modern Best Practices (2024-2025):

• Tree-based methods (LightGBM) deliver best performance + efficiency
• Transformers excel at long-term dependencies but watch for distribution shifts
• Future-Guided Learning: 44.8% AUC-ROC improvement in event forecasting
• Explainability critical in healthcare/finance (use LightGBM + SHAP)

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.

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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 Future-Guided Learning 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
• ai-mlops - Security, privacy, governance for ML systems

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

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

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Quick Reference

Task	Tool/Framework	Command	When to Use
TS EDA & Decomposition	Pandas, statsmodels	seasonal_decompose(), df.plot()	Identifying trend, seasonality, outliers
Lag/Rolling Features	Pandas, NumPy	df.shift(), df.rolling()	Creating temporal features for ML models
Model Training (Tree-based)	LightGBM, XGBoost	lgb.train(), xgb.train()	Tabular TS with seasonality, covariates
Deep Learning (Transformers)	TimesFM, Chronos	model.forecast()	Long-term dependencies, complex patterns
Future-Guided Learning	Custom RNN/Transformer	Feedback-based training	Event forecasting (44.8% AUC-ROC improvement)
Backtesting	Custom rolling windows	for window in windows: train(), test()	Temporal validation without leakage
Metrics Evaluation	scikit-learn, custom	mean_absolute_error(), MAPE, MASE	Multi-horizon forecast accuracy
Production Deployment	MLflow, Airflow	Scheduled pipelines	Automated retraining, drift monitoring

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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)?
    │   └─ Future-Guided Learning → 44.8% AUC-ROC improvement
    │
    ├─ 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

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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 (2024-2025 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)
  • Future-Guided Learning (44.8% AUC-ROC boost for events)
  • 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

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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 - Chronos, TimesFM, Future-Guided Learning implementation

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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, automated monitoring (18-second drift detection), 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

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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)

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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 resources/ for detailed implementation patterns
• Use templates/ for copy-paste ready code
• Always check for temporal leakage (future data in training)
• Prefer LightGBM for most use cases unless long-term dependencies require Transformers
• 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.