AR-MGNet: Adaptive Macro-Micro Reconciliation Multi-Grid Network for General Spatio-Temporal Forecasting

Spatio-temporal forecasting underpins numerous real-world applications, yet existing methods struggle to jointly achieve global-scale efficiency and local-scale fidelity, particularly when system states shift or abrupt events occur. Most approaches rely on fixed spatial partitions or uniform refinement strategies, limiting their ability to adapt to dynamically changing functional regions. To overcome these limitations, we propose AR-MGNet, an Adaptive Macro-Micro Reconciliation Multi-Grid Network that reformulates forecasting as a bidirectional macro-micro reconciliation process. AR-MGNet first aggregates fine-grained observations into functional macro-grids to efficiently solve a coherent macroscopic future, then selectively recovers unresolved micro-scale innovations in event-critical regions through a top-down adaptive refinement pathway. A dynamic multi-grid neighborhood learning module further captures scale-aware spatial dependencies by constructing sparse, relation-aware adjacency matrices that adapt to the current spatial regime. Across four LargeST traffic benchmarks and five diverse general spatio-temporal datasets (wind, air quality, EV charging, electricity, solar), AR-MGNet achieves the best performance in all traffic and most cross-domain datasets, with ablation studies confirming the contribution of each core mechanism. These results demonstrate that adaptive macro-micro reconciliation offers a unified principle for general spatio-temporal forecasting.