Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
A new framework has been proposed to enhance spatiotemporal prediction performance by addressing the limitations of existing methods. This framework harmonizes spatial and temporal feature representations, leading to significant accuracy improvements across various domains. The research highlights the importance of dimensional balance in overcoming prediction uncertainties in complex datasets.
- ▪The proposed framework compresses spatial dimensionality while extending temporal horizons to capture long-range dependencies.
- ▪Empirical results show that larger mismatches in spatiotemporal complexity lead to higher prediction uncertainties.
- ▪Extensive experiments demonstrate substantial accuracy gains in urban traffic, meteorological, and epidemic datasets.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18793 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.18793 (cs) [Submitted on 11 May 2026] Title:Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance Authors:Jing Chen, Shixiang Pan, Yujie Fan, Haocheng Ye, Haitao Xu, Wenqiang Xu View a PDF of the paper titled Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance, by Jing Chen and 5 other authors View PDF HTML (experimental) 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.
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