Noise-contrastive Online Change Point Detection

TL;DR

This paper introduces a new online change point detection method based on discrepancy measures, providing theoretical guarantees and demonstrating effectiveness on synthetic and real data.

Contribution

It presents a flexible, discrepancy-based online change point detection algorithm with proven non-asymptotic bounds and practical validation.

Findings

Algorithm performs well on synthetic data

Effective on real-world datasets

Provides theoretical bounds on detection delay

Abstract

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 flexible algorithms 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.