通过最优传输引导的掩码解决时空预测中的空间不可区分性
Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking
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中文总结 AI 辅助
针对时空预测中的空间不可区分性问题，提出基于最优传输引导掩码的自监督框架STOT，通过相似性感知度量和掩码策略提升预测性能，并在六个数据集上验证了有效性。
中文摘要 AI 辅助
时空预测旨在从空间结构上的相关时间信号中学习判别性表示，以实现准确的未来推断。一个核心挑战是“空间不可区分性”：不同节点可能共享相似的历史模式，却朝着不同的未来演化，这严重降低了真实世界传感器网络中预测的性能。现有的基于嵌入和图神经网络（GNN）的方法可以部分检测此类模糊节点，但依赖于历史相似性，难以捕捉“未来行为分歧”。我们提出了STOT（时空最优传输），一个自监督框架，通过最优传输引导的结构化掩码来解决时空模糊性。我们的关键思想是将不可区分性视为一个“去歧义”问题：通过利用并发空间相关性及其时变相似性来推断未来状态。我们设计了一个用于动态节点间关系的相似性感知度量，以及一种基于最优传输的掩码策略，以在预训练期间强调模糊位置。一个批次一致性约束保持了语义一致性，而随机游走掩码机制促进了结构化上下文探索。在六个真实世界数据集上的实验表明，STOT在评估基准上与最先进的基线方法表现相当，并通过传输计划可视化提高了可解释性。
英文摘要
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.
发表机构
- Dongbei University of Finance and Economics（东北财经大学）
- University of Liverpool（利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。
