Ensembles of detectors for online detection of transient changes

Alexey Artemov, Evgeny Burnaev

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

20 Citations (Scopus)


Classical change-point detection procedures assume a change-point model to be known and a change consisting in establishing a new observations regime, i.e. The change lasts infinitely long. These modeling assumptions contradicts applied problems statements. Therefore, even theoretically optimal statistics in practice very often fail when detecting transient changes online. In this work in order to overcome limitations of classical change-point detection procedures we consider approaches to constructing ensembles of change-point detectors, i.e. algorithms that use many detectors to reliably identify a change-point. We propose a learning paradigm and specific implementations of ensembles for change detection of short-term (transient) changes in observed time series. We demonstrate by means of numerical experiments that the performance of an ensemble is superior to that of the conventional change-point detection procedures.

Original languageEnglish
Title of host publicationEighth International Conference on Machine Vision, ICMV 2015
EditorsAntanas Verikas, Petia Radeva, Dmitry Nikolaev
ISBN (Electronic)9781510601161
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
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference8th International Conference on Machine Vision, ICMV 2015


  • aggregation
  • blending
  • change-point detection
  • ensemble
  • logistic regression
  • transient changes


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