Editing AI for Time Series and Forecasting (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a production time series forecasting system:

'''1. Data architecture'''
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Raw time series sources (databases, IoT, APIs)
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[Time-indexed storage: InfluxDB, TimescaleDB, or Parquet partitioned by date]
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[Feature engineering pipeline:]
│   ├── Temporal features: hour, day of week, month, quarter
│   ├── Lag features: lag-1, lag-7, lag-28, rolling mean/std
│   ├── Fourier features for seasonality
│   └── External covariates: weather, holidays, promotions
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[Stationarity tests + differencing if needed]
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[Train/val/test split: chronological]
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'''2. Model training and selection'''
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Train multiple models (baseline naive, SARIMA, TFT, foundation model)
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Evaluate each with rolling window backtesting
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Select winner by held-out test MAPE and CRPS
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Train ensemble: weighted average of top-3 models (often beats any single model)
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Register in model registry with evaluation metrics
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'''3. Production serving and retraining'''
* Serve forecasts via API with caching (same-day forecast is rarely regenerated)
* Nightly retrain on latest data window (rolling retrain strategy)
* Monitor forecast accuracy vs. actuals in real-time; alert on anomalies
* Detect distribution shift: plot forecast distribution vs. actuals weekly
* Trigger manual review when MAE exceeds historical 95th percentile

[[Category:Artificial Intelligence]]
[[Category:Machine Learning]]
[[Category:Time Series]]
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