Demand Forecasting
Predict passenger volumes, energy loads, retail sales, and resource needs using Bayesian temporal models with seasonal and trend components.
Why Bayesian for Demand Forecasting?
Demand data often has seasonal patterns, trends, and irregular fluctuations that benefit from Bayesian modeling:
- Seasonal decomposition separates trend from periodic patterns with full uncertainty
- Credible intervals quantify forecast uncertainty for capacity planning
- Multiple model comparison via marginal likelihood selects the best structure
- Interpretable components provide business insights, not just predictions