Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
Summary
This paper proposes a framework that uses entropy-based diagnostics to harmonize spatial and temporal feature representations, achieving substantial accuracy gains on large-scale spatiotemporal prediction tasks across urban traffic, meteorology, and epidemic datasets.
View Cached Full Text
Cached at: 05/20/26, 08:34 AM
# Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance Source: [https://arxiv.org/abs/2605.18793](https://arxiv.org/abs/2605.18793) [View PDF](https://arxiv.org/pdf/2605.18793)[HTML \(experimental\)](https://arxiv.org/html/2605.18793v1) > 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[this https URL](https://github.com/ST-Balance/ST-Balance)\. ## Submission history From: Jing Chen \[[view email](https://arxiv.org/show-email/ea3a0d57/2605.18793)\] **\[v1\]**Mon, 11 May 2026 06:29:26 UTC \(26,055 KB\)
Similar Articles
Nested Spatio-Temporal Time Series Forecasting
This paper proposes a nested spatiotemporal forecasting framework that uses spectral clustering to construct semantically coherent macro-level regions, which provide top-down guidance for fine-grained micro-level predictions. Experiments on high-dimensional datasets show consistent improvements over state-of-the-art baselines.
A Granularity-Aware EEG Feature Framework for Psychopathology Dimension Prediction
This paper presents a granularity-aware EEG feature framework that organizes multi-scale descriptors into global, regional, and channel levels to predict dimensional psychopathology. Using the HBN cohort, it shows that tree-based models and granularity-balanced feature selection yield modest improvements, suggesting multi-scale EEG features contain weak but detectable signals for pediatric mental health.
Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation
This paper introduces Temporal-Spatial Parallel Decoding (TSPD) and Confidence Extrapolation (CE) to accelerate inference in diffusion-based large language models by dynamically deciding when tokens have converged and forecasting logit trends, reducing unnecessary denoising steps while preserving output quality.
Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting
This paper introduces regime-stratified evaluation for time series foundation models, revealing that aggregate metrics hide severe failures during traffic regime transitions, and proposes bimodal mixture augmentation to improve coverage while preserving overall accuracy.
Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift
This paper investigates temporal out-of-distribution shift in deep-learning-based climate downscaling and proposes a domain-adaptive framework that combines supervised reconstruction with domain alignment to improve high-resolution climate projections under non-stationary conditions.