الملخص
Spatiotemporal prediction aims to learn discriminative representations from correlated temporal signals over spatial structures for accurate future inference. A central challenge is \emph{spatial indistinguishability}: different nodes may share similar historical patterns yet evolve toward divergent futures, severely degrading forecasting performance in real-world sensor networks. Existing embedding-based and graph neural network (GNN)-based approaches can partially detect such ambiguous nodes but rely on historical similarity, struggling to capture \emph{future behavioral divergence}. We propose \textbf{STOT} (\textbf{S}patio\textbf{T}emporal \textbf{O}ptimal \textbf{T}ransport), a self-supervised framework that resolves spatiotemporal ambiguity via structured masking guided by optimal transport. Our key idea treats indistinguishability as a \emph{disambiguation} problem: future states are inferred by exploiting concurrent spatial correlations and their time-varying similarity. We design a similarity-aware metric for dynamic inter-node relationships and an optimal transport-based masking strategy to emphasize ambiguous positions during pre-training. A batch consistency constraint preserves semantic coherence, while a random-walk masking mechanism promotes structured context exploration. Experiments on six real-world datasets show that STOT performs competitively with state-of-the-art baselines on the evaluated benchmarks and improved interpretability through transport-plan visualizations.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Wang, G., & Tong, J. (2026). Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking. https://omanscience.com/ar/articles/addressing-spatial-indistinguishability-in-spatiotemporal-prediction-via-optimal-transport-guided-masking
MLA 9
Wang, Guangyu, and Jiawei Tong. "Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking." https://omanscience.com/ar/articles/addressing-spatial-indistinguishability-in-spatiotemporal-prediction-via-optimal-transport-guided-masking.
شيكاغو (المؤلف–التاريخ)
Wang, Guangyu, and Jiawei Tong. 2026. "Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking." https://omanscience.com/ar/articles/addressing-spatial-indistinguishability-in-spatiotemporal-prediction-via-optimal-transport-guided-masking.
هارفارد
Wang, G. and Tong, J. (2026) 'Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking', Available at: https://omanscience.com/ar/articles/addressing-spatial-indistinguishability-in-spatiotemporal-prediction-via-optimal-transport-guided-masking.
فانكوفر
Wang G, Tong J. Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking. https://omanscience.com/ar/articles/addressing-spatial-indistinguishability-in-spatiotemporal-prediction-via-optimal-transport-guided-masking
IEEE
G. Wang, and J. Tong, "Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking," https://omanscience.com/ar/articles/addressing-spatial-indistinguishability-in-spatiotemporal-prediction-via-optimal-transport-guided-masking.