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
| 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
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
Forecasting Strategies
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
Advanced Forecasting
Navigation: Templates (Copy-Paste Ready)
Data Preparation
Feature Templates
Model Templates
Evaluation Templates
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.
Fact-Checking
- Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
- Prefer primary sources; report source links and dates for volatile information.
- If web access is unavailable, state the limitation and mark guidance as unverified.
