ai-ml-timeseries

What this skill does

# 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.

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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 temporal classification/survival modelling for event prediction."

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

- [ai-ml-data-science](../ai-ml-data-science/SKILL.md) - General data science workflows, EDA, feature engineering, evaluation
- [ai-mlops](../ai-mlops/SKILL.md) - Model deployment, monitoring, drift detection, retraining automation

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

- [ai-llm](../ai-llm/SKILL.md) - LLM fine-tuning, prompting, evaluation
- [ai-rag](../ai-rag/SKILL.md) - RAG pipeline design and optimization

---

## 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 (Sequence models) | Transformers, RNNs | `model.forecast()` | Long-term dependencies, complex patterns |
| Event forecasting | Binary/time-to-event models | Temporal labeling + rolling validation | Sparse events and alerts |
| 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 |

---

## Decision Tree: Choosing Time Series Approach

```text
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](references/ts-eda-best-practices.md)**
  - Frequency detection, missing timestamps, decomposition
  - Outlier detection, level shifts, seasonality analysis
  - Granularity selection and stability checks

### Feature Engineering

- **[Lag & Rolling Patterns](references/lag-rolling-patterns.md)**
  - 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](references/model-selection-guide.md)**
  - 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](references/lightgbm-ts-patterns.md)** *(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](references/multistep-forecasting-patterns.md)**
  - Direct strategy (separate models per horizon)
  - Recursive strategy (feed predictions back)
  - Seq2Seq strategy (Transformers, RNNs for long horizons)

- **[Intermittent Demand Patterns](references/intermittent-demand-patterns.m