Robust and efficient change point detection using novel multivariate rank-energy GoF test

TL;DR

This paper introduces a novel entropy-regularized multivariate GoF test called soft-Rank Energy (sRE) for change point detection, improving sensitivity, computational efficiency, and accuracy over existing methods.

Contribution

The paper develops and demonstrates a new sRE test based on entropy-regularized OT, enhancing change point detection performance and efficiency.

Findings

sRE outperforms existing methods in AUC and F1-score

sRE reduces false alarms caused by small distribution changes

sRE is computationally more efficient

Abstract

In this paper, we use and further develop upon a recently proposed multivariate, distribution-free Goodness-of-Fit (GoF) test based on the theory of Optimal Transport (OT) called the Rank Energy (RE) [1], for non-parametric and unsupervised Change Point Detection (CPD) in multivariate time series data. We show that directly using RE leads to high sensitivity to very small changes in distributions (causing high false alarms) and it requires large sample complexity and huge computational cost. To alleviate these drawbacks, we propose a new GoF test statistic called as soft-Rank Energy (sRE) that is based on entropy regularized OT and employ it towards CPD. We discuss the advantages of using sRE over RE and demonstrate that the proposed sRE based CPD outperforms all the existing methods in terms of Area Under the Curve (AUC) and F1-score on real and synthetic data sets.