Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance

This paper proposes a scalable, adaptive framework to improve spatiotemporal prediction by harmonizing spatial and temporal feature representations. It addresses bottlenecks in existing methods through spatial and temporal entropy measures to tackle complexity mismatch and prediction uncertainty.