Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes

• URL: http://arxiv.org/abs/2602.04798v1
• Date: Wed, 04 Feb 2026 17:44:41 GMT
• Title: Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes
• Authors: Wenbin Zhou, Liyan Xie, Shixiang Zhu,
• Abstract summary: We propose a likelihood-free, score-based detection framework that jointly estimates the change time and the change region in continuous space-time.<n> operating sequentially the procedure both outputs a stopping time and an estimated change region, enabling real-time detection with spatial interpretability.
• Score: 14.068679218846894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study sequential change-point detection for spatio-temporal point processes, where actionable detection requires not only identifying when a distributional change occurs but also localizing where it manifests in space. While classical quickest change detection methods provide strong guarantees on detection delay and false-alarm rates, existing approaches for point-process data predominantly focus on temporal changes and do not explicitly infer affected spatial regions. We propose a likelihood-free, score-based detection framework that jointly estimates the change time and the change region in continuous space-time without assuming parametric knowledge of the pre- or post-change dynamics. The method leverages a localized and conditionally weighted Hyvärinen score to quantify event-level deviations from nominal behavior and aggregates these scores using a spatio-temporal CUSUM-type statistic over a prescribed class of spatial regions. Operating sequentially, the procedure outputs both a stopping time and an estimated change region, enabling real-time detection with spatial interpretability. We establish theoretical guarantees on false-alarm control, detection delay, and spatial localization accuracy, and demonstrate the effectiveness of the proposed approach through simulations and real-world spatio-temporal event data.

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