[
    {
        "id": "osp-22136",
        "type": "article-journal",
        "title": "Addressing Spatial Indistinguishability in Spatiotemporal Prediction via Optimal Transport-Guided Masking",
        "author": [
            {
                "family": "Wang",
                "given": "Guangyu"
            },
            {
                "family": "Tong",
                "given": "Jiawei"
            }
        ],
        "URL": "https://omanscience.com/en/articles/addressing-spatial-indistinguishability-in-spatiotemporal-prediction-via-optimal-transport-guided-masking",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]