Modelling recurrent events for improving online change detection

Alexandr Maslov, Mykola Pechenizkiy, Indre Zliobaite, Tommi Kärkkäinen

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

2 Citations (Scopus)

Abstract

The task of online change point detection in sensor data streams is often complicated due to presence of noise that can be mistaken for real changes and therefore affecting performance of change detectors. Most of the existing change detection methods assume that changes are independent from each other and occur at random in time. In this paper we study how performance of detectors can be improved in case of recurrent changes. We analytically demonstrate under which conditions and for how long recurrence information is useful for improving the detection accuracy. We propose a simple computationally efficient message passing procedure for calculating a predictive probability distribution of change occurrence in the future. We demonstrate two straightforward ways to apply the proposed procedure to existing change detection algorithms. Our experimental analysis illustrates the effectiveness of these approaches in improving the performance of a baseline online change detector by incorporating recurrence information.

Original languageEnglish
Title of host publicationProceedings of the 2016 SIAM International Conference on Data Mining
Pages549-557
Number of pages9
ISBN (Electronic)978-1-61197-434-8
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventSIAM International Conference on Data Mining - Miami, United States
Duration: 5 May 20167 May 2016
Conference number: 16

Conference

ConferenceSIAM International Conference on Data Mining
Abbreviated titleSDM
CountryUnited States
CityMiami
Period05/05/201607/05/2016

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