Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance

2026-05-11

AI Summary

Existing spatiotemporal forecasting methods suffer from a mismatch between model capacity and the inherent complexity of spatiotemporal dynamics, leading to performance bottlenecks and poor cross-domain generalization. This work proposes an adaptive dimensionality coordination framework that, for the first time, employs spatiotemporal entropy not as an optimization objective but as a diagnostic tool to identify such complexity mismatches. Guided by this insight, the framework dynamically balances spatial and temporal representations: it compresses spatial dimensions via low-rank matrix embeddings to preserve essential structural information while expanding the temporal horizon to capture long-range dependencies and mitigate error accumulation. Without increasing model capacity, the approach achieves substantial improvements in prediction accuracy and generalization across diverse domains—including traffic, meteorology, and epidemiology—demonstrating its broad applicability.

Abstract

Accurate spatiotemporal pattern analysis is critical in fields such as urban traffic, meteorology, and public health monitoring. However, existing methods face performance bottlenecks, typically yielding only incremental gains and often exhibiting limited cross-domain transferability. We analyze this bottleneck through spatial and temporal entropy measures, which are used as diagnostic indicators of spatiotemporal complexity mismatch rather than as guarantees that entropy alignment alone yields better forecasting. Empirically, larger mismatch is often accompanied by higher prediction uncertainty, especially under a fixed model-capacity budget. Guided by this diagnostic, we propose a scalable, adaptive framework that harmonizes spatial and temporal feature representations. Spatial dimensionality is compressed via low-rank matrix embedding to preserve essential structure, while an extended temporal horizon captures long-range dependencies and mitigates cumulative errors arising from temporal heterogeneity. Extensive experiments on urban traffic, meteorological, and epidemic datasets demonstrate substantial accuracy gains and broad applicability across the evaluated domains, suggesting that the framework is promising for a wide range of spatiotemporal tasks beyond the current study. The code is available on GitHub at https://github.com/ST-Balance/ST-Balance.

Problem

• spatiotemporal prediction
• performance bottleneck
• cross-domain transferability
• complexity mismatch
• prediction uncertainty

Innovation

• spatiotemporal prediction
• dimensional balance
• low-rank embedding
• temporal horizon extension
• entropy-based diagnosis