TY - GEN
T1 - Correlation-aware Online Change Point Detection
AU - Deng, Chengyuan
AU - Chen, Zhengzhang
AU - Zhao, Xujiang
AU - Wang, Haoyu
AU - Wang, Junxiang
AU - Gao, Jie
AU - Chen, Haifeng
N1 - Publisher Copyright: © 2025 Copyright held by the owner/author(s).
PY - 2025/11/10
Y1 - 2025/11/10
N2 - Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where different types of change can occur, including shifts in both the marginal and joint distributions of the data. In this paper, we address these challenges by tracking the Riemannian geometry of correlation matrices, allowing Riemannian metrics to compute the geodesic distance as an accurate measure of correlation dynamics. We introduce Rio-CPD, a correlation-aware online change point detection framework that integrates the Riemannian geometry of the manifold of symmetric positive definite matrices with the cumulative sum (CUSUM) statistic for detecting change points. Rio-CPD employs a novel CUSUM design by computing the geodesic distance between current observations and the Fréchet mean of prior observations. With appropriate choices of Riemannian metrics, Rio-CPD offers a simple yet effective and computationally efficient algorithm. We also provide a theoretical analysis on standard metrics for change point detection within Rio-CPD. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods on detection accuracy, average detection delay, and efficiency.
AB - Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where different types of change can occur, including shifts in both the marginal and joint distributions of the data. In this paper, we address these challenges by tracking the Riemannian geometry of correlation matrices, allowing Riemannian metrics to compute the geodesic distance as an accurate measure of correlation dynamics. We introduce Rio-CPD, a correlation-aware online change point detection framework that integrates the Riemannian geometry of the manifold of symmetric positive definite matrices with the cumulative sum (CUSUM) statistic for detecting change points. Rio-CPD employs a novel CUSUM design by computing the geodesic distance between current observations and the Fréchet mean of prior observations. With appropriate choices of Riemannian metrics, Rio-CPD offers a simple yet effective and computationally efficient algorithm. We also provide a theoretical analysis on standard metrics for change point detection within Rio-CPD. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods on detection accuracy, average detection delay, and efficiency.
KW - aiops
KW - change point detection
KW - cusum
KW - riemannian geometry
UR - https://www.scopus.com/pages/publications/105023170765
UR - https://www.scopus.com/pages/publications/105023170765#tab=citedBy
U2 - 10.1145/3746252.3761021
DO - 10.1145/3746252.3761021
M3 - Conference contribution
T3 - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
SP - 520
EP - 530
BT - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery, Inc
T2 - 34th ACM International Conference on Information and Knowledge Management, CIKM 2025
Y2 - 10 November 2025 through 14 November 2025
ER -