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

arXiv: https://arxiv.org/abs/2602.04798 · HTML: https://mlsanity.com/paper/2602.04798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 73 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 25 of 25)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications](https://mlsanity.com/paper/1708.02647/) (2017)
- [COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution](https://mlsanity.com/paper/1507.02293/) (2015)
- [Estimating Density Models with Truncation Boundaries using Score Matching](https://mlsanity.com/paper/1910.03834/) (2019)
- [High-dimensional, multiscale online changepoint detection](https://mlsanity.com/paper/2003.03668/) (2020)
- [Autoregressive Score Matching](https://mlsanity.com/paper/2010.12810/) (2020)
- [Spatial-Temporal-Textual Point Processes for Crime Linkage Detection](https://mlsanity.com/paper/1902.00440/) (2019)
- [Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling](https://mlsanity.com/paper/2005.08665/) (2020)
- [Sequential change-point detection for mutually exciting point processes over networks](https://mlsanity.com/paper/2102.05724/) (2021)
- [Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes](https://mlsanity.com/paper/2305.12569/) (2023)
- [SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process](https://mlsanity.com/paper/2310.16336/) (2023)
- [Spatio-Temporal Wildfire Prediction using Multi-Modal Data](https://mlsanity.com/paper/2207.13250/) (2022)
- [Quickest Change Detection for Unnormalized Statistical Models](https://mlsanity.com/paper/2302.00250/) (2023)
- [Is Score Matching Suitable for Estimating Point Processes?](https://mlsanity.com/paper/2410.04037/) (2024)
- [Sequential change-point detection in high-dimensional Gaussian graphical models](https://mlsanity.com/paper/1806.07870/) (2018)
- [Diffusion-Based Hypothesis Testing and Change-Point Detection](https://mlsanity.com/paper/2506.16089/) (2025)
- [Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors](https://mlsanity.com/paper/1901.08196/) (2019)
- [Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes](https://mlsanity.com/paper/2211.12091/) (2022)
- [Sequential Change Point Detection via Denoising Score Matching](https://mlsanity.com/paper/2501.12667/) (2025)
- [Robust Score-Based Quickest Change Detection](https://mlsanity.com/paper/2407.11094/) (2024)
- [Scanning a Poisson Random Field for Local Signals](https://mlsanity.com/paper/1406.3258/) (2014)
- [Conditional Score Learning for Quickest Change Detection in Markov Transition Kernels](https://mlsanity.com/paper/2511.03953/) (2025)
- [Score-Based Quickest Change Detection and Fault Identification for Multi-Stream Signals](https://mlsanity.com/paper/2511.03967/) (2025)

## Built on by (most cited first, 1 of 1)

- [Learning to Test: Latent Representation Learning for Dynamic Instability Detection](https://mlsanity.com/paper/2604.10967/) (2026)

## Abstract (arXiv)

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 Hyv"arinen 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.