Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting

TL;DR - HierSTT is a hierarchical Transformer that jointly forecasts emergency-department demand at hospital, regional, and national levels while enforcing consistency across forecasts. It could improve coordinated staffing, bed management, and system-wide capacity planning.

• Combines a Temporal Fusion Transformer for national trends with spatio-temporal encoder-decoder modules for regional and hospital demand.
• Uses a coherence-aware loss to penalize forecasts that do not aggregate consistently across levels.
• Evaluated on a new Portuguese dataset spanning 81 hospitals and five regional health administrations.
• Reduced average WAPE by 32% versus the best non-hierarchical deep-learning baseline and outperformed classical hierarchical reconciliation methods.