Public articles linked to the same research event.
arXiv The work proposes HierSTT, an end-to-end hierarchical Transformer framework that uses a Temporal Fusion Transformer for national-level ED demand and spatio-temporal Transformer encoder-decoders to produce regional and hospital forecasts conditioned on higher-level predictions, with a coherence-aware loss penalizing cross-level inconsistency; it also releases a nationwide Portuguese ED dataset covering 81 hospitals across 5 regional health administrations with heterogeneous level-specific covariates, and reports a 32% average WAPE reduction versus the best non-hierarchical deep learning baseline, outperforming classical hierarchical reconciliation methods while producing near-coherent predictions across levels.
The work proposes HierSTT, an end-to-end hierarchical Transformer framework that uses a Temporal Fusion Transformer for national-level ED demand and spatio-temporal Transformer encoder-decoders to produce regional and hospital forecasts conditioned on higher-level predictions, with a coherence-aware loss penalizing cross-level inconsistency; it also releases a nationwide Portuguese ED dataset covering 81 hospitals across 5 regional health administrations with heterogeneous level-specific covariates, and reports a 32% average WAPE reduction versus the best non-hierarchical deep learning baseline, outperforming classical hierarchical reconciliation methods while producing near-coherent predictions across levels.
The work proposes HierSTT, an end-to-end hierarchical Transformer framework that uses a Temporal Fusion Transformer for national-level ED demand and spatio-temporal Transformer encoder-decoders to produce regional and hospital forecasts conditioned on higher-level predictions, with a coherence-aware loss penalizing cross-level inconsistency; it also releases a nationwide Portuguese ED dataset covering 81 hospitals across 5 regional health administrations with heterogeneous level-specific covariates, and reports a 32% average WAPE reduction versus the best non-hierarchical deep learning baseline, outperforming classical hierarchical reconciliation methods while producing near-coherent predictions across levels.
The work proposes HierSTT, an end-to-end hierarchical Transformer framework that uses a Temporal Fusion Transformer for national-level ED demand and spatio-temporal Transformer encoder-decoders to produce regional and hospital forecasts conditioned on higher-level predictions, with a coherence-aware loss penalizing cross-level inconsistency; it also releases a nationwide Portuguese ED dataset covering 81 hospitals across 5 regional health administrations with heterogeneous level-specific covariates, and reports a 32% average WAPE reduction versus the best non-hierarchical deep learning baseline, outperforming classical hierarchical reconciliation methods while producing near-coherent predictions across levels.