RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection
This work addresses the challenge of detecting abrupt shifts in data sequences for applications requiring real-time monitoring, presenting an incremental improvement with a novel integration of Riemannian metrics.
The paper tackles the problem of online change point detection by tracking correlation dynamics using Riemannian geometry, resulting in a method that outperforms existing approaches in accuracy, detection delay, and efficiency on synthetic and real-world datasets.
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 changes 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 non-parametric, 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. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods on detection accuracy, average detection delay and efficiency.