RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection

TL;DR

This paper introduces Rio-CPD, a novel online change point detection method leveraging Riemannian geometry of correlation matrices to improve detection accuracy and efficiency in dynamic data environments.

Contribution

The paper presents a new correlation-aware, non-parametric online change point detection framework using Riemannian geometry and a novel CUSUM design based on geodesic distances.

Findings

Outperforms existing methods in detection accuracy

Reduces average detection delay

Demonstrates computational efficiency on real-world datasets

Abstract

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 Fr\'echet 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.