Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
Balancing spatial and temporal dimensions enhances large-scale prediction accuracy.
Existing spatiotemporal models struggle with cross-domain performance and incremental gains. This research introduces dimensional balance, using entropy measures to diagnose and improve prediction accuracy in urban traffic, weather, and health monitoring. The approach addresses bottlenecks by optimizing spatial-temporal interactions.