Online Change Point Detection with Kernels - Observatoire de la Cote d'Azur Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2021

Online Change Point Detection with Kernels

André Ferrari
Cédric Richard
Anthony Bourrier
Ikram Bouchikhi
  • Fonction : Auteur
  • PersonId : 1110471

Résumé

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.
Fichier principal
Vignette du fichier
2002.02704.pdf (1.09 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03347266 , version 1 (17-09-2021)

Identifiants

  • HAL Id : hal-03347266 , version 1

Citer

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

Relations

79 Consultations
26 Téléchargements

Partager

Gmail Facebook X LinkedIn More