Online change-point detection with kernels

Change-points in time series data are usually defined as the time instants at which changes in their properties occur. Detecting change-points is critical in a number of applications as diverse as detecting credit card and insurance frauds, or intrusions into networks. Recently the authors introduced an online kernelbased change-point detection method built upon direct estimation of the density ratio on consecutive time intervals. This paper further investigates this algorithm, making improvements and analyzing its behavior in the mean and mean square sense, in the absence and presence of a change point. These theoretical analyses are validated with Monte Carlo simulations. The detection performance of the algorithm is illustrated through experiments on real-world data and compared to state of the art methodologies. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates a kernel-based change-point detection method that estimates the density ratio on consecutive time intervals. The algorithm is improved and its behavior is analyzed in terms of mean and mean square. The effectiveness of the algorithm is validated through Monte Carlo simulations and experiments on real-world data.