Nonparametric decomposition of quasi-periodic time series for change-point detection

Alexey Artemov, Evgeny Burnaev, Andrey Lokot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

The paper is concerned with the sequential online change-point detection problem for a dynamical system driven by a quasiperiodic stochastic process. We propose a multicomponent time series model and an effective online decomposition algorithm to approximate the components of the models. Assuming the stationarity of the obtained components, we approach the change-point detection problem on a per-component basis and propose two online change-point detection schemes corresponding to two real-world scenarios. Experimental results for decomposition and detection algorithms for synthesized and real-world datasets are provided to demonstrate the efficiency of our change-point detection framework.

Original languageEnglish
Title of host publicationEighth International Conference on Machine Vision, ICMV 2015
EditorsAntanas Verikas, Petia Radeva, Dmitry Nikolaev
PublisherSPIE
Volume9875
ISBN (Electronic)9781510601161
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event8th International Conference on Machine Vision, ICMV 2015 - Barcelona, Spain
Duration: 19 Nov 201521 Nov 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9875
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference8th International Conference on Machine Vision, ICMV 2015
Country/TerritorySpain
CityBarcelona
Period19/11/1521/11/15

Keywords

  • anomaly detection
  • kernel regression
  • quasiperiodic time series

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