Dimensional balance improves large scale spatiotemporal prediction performance

Area of Science:

• Data Science
• Computational Science
• Geospatial Analysis

Background:

• Accurate spatiotemporal pattern analysis is crucial for urban traffic, meteorology, and public health.
• Existing methods often face performance bottlenecks and limited cross-domain transferability.
• Spatiotemporal complexity mismatch and prediction uncertainty are key challenges.

Purpose of the Study:

• To analyze the bottleneck in spatiotemporal pattern analysis using entropy measures.
• To develop a scalable, adaptive framework for harmonizing spatial and temporal feature representations.
• To improve forecasting accuracy and cross-domain applicability.

Main Methods:

• Analysis of spatial and temporal entropy measures to diagnose complexity mismatch.
• Development of a framework with compressed spatial dimensionality via low-rank matrix embedding.
• Incorporation of an extended temporal horizon to capture long-range dependencies.

Main Results:

• Demonstrated substantial accuracy gains in urban traffic, meteorological, and epidemic datasets.
• Showcased broad applicability and improved prediction accuracy across diverse domains.
• Validated the framework's effectiveness under fixed model-capacity budgets.

Conclusions:

• The proposed framework effectively harmonizes spatial and temporal features for enhanced spatiotemporal analysis.
• The approach offers significant improvements over existing methods, addressing performance bottlenecks.
• The framework shows promise for a wide range of spatiotemporal tasks beyond the current study.