Paper ID: 2206.10143

A Contrastive Approach to Online Change Point Detection

Artur Goldman, Nikita Puchkin, Valeriia Shcherbakova, Uliana Vinogradova

We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to a flexible procedure suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets.

Submitted: Jun 21, 2022