Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance

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

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ethics.ai (11 May 2026), “Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance,” evidence record 4589, https://ethics.ai/record/4589 (originally published by arXiv).

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