Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Online Change Point Detection with Kernels

Abstract : 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 kernel-based 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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03347266
Contributor : Cédric Richard Connect in order to contact the contributor
Submitted on : Friday, September 17, 2021 - 10:02:30 AM
Last modification on : Friday, November 19, 2021 - 3:36:28 AM

File

2002.02704.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03347266, version 1

Citation

André Ferrari, Cédric Richard, Anthony Bourrier, Ikram Bouchikhi. Online Change Point Detection with Kernels. 2021. ⟨hal-03347266⟩

Share

Metrics

Record views

47

Files downloads

16