Accurate demand forecasting across multiple aggregation levels is essential for managing complex supply networks, where operations must balance inventory costs, service levels, and resource coordination under non-stationary and heterogeneous demand patterns. Existing spatiotemporal models typically treat all forecasting units at a single resolution, obscuring inherent hierarchical structures and often producing inconsistent predictions across levels. This study proposes a Hierarchical Hybrid Spatio-Temporal Demand Forecasting (H2SDF) architecture that formulates multi-granularity forecasting as a coupled system-of-systems problem. H2SDF decomposes the task into three coordinated layers. At the macro layer, a frequency-aware model extracts global trends and multi-scale periodicities from aggregate demand, providing a stable system-level reference. At the meso layer, a Transformer-based multi-task learner disaggregates the macro signal into location-specific forecasts while learning dynamic inter-location dependencies via self-attention, avoiding reliance on predefined static graphs. At the micro layer, gradient-boosted tree models refine category-level predictions by fusing upstream signals with contextual covariates to correct residual errors. A top-down coupling mechanism propagates forecasts and consistency constraints across layers. Experiments on a 2976 h real-world dataset with 18 locations and 8 product categories demonstrate that H2SDF reduces RMSE and improves R2 compared with state-of-the-art baselines across all three granularities. The results confirm that hierarchical decomposition with heterogeneous model synergy effectively mitigates demand uncertainty and strengthens decision support for inventory, logistics, and workforce planning.
Nie et al. (Fri,) studied this question.