A novel discords detector for periodic time series based on weighted spherical single means with phase shift

Jun Wang, Fu Lai Korris Chung, Shi Tong Wang, Zhao Hong Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The traditional one-class classifiers are not suitable for detecting discords in periodic time series. A novel one-class classifier PS-WS1M-OCC is proposed in this paper. In our method, the phase problem in time series is solved by introducing phase shift into the clustering procedure. Meanwhile, a novel criterion for adaptively choosing threshold is proposed. In this way, the proposed classifier is insensitive to noise in the training set. Experimental results show that our PS-WSKM-OCC is more robust than the existing one-class classifiers when it is applied to the problem of discord detection in the periodic time series.
Original languageEnglish
Pages (from-to)984-992
Number of pages9
JournalZidonghua Xuebao/Acta Automatica Sinica
Volume37
Issue number8
DOIs
Publication statusPublished - 1 Aug 2011

Keywords

  • Discord detection
  • Learning from noise data
  • One-class classifier
  • Weighted spherical single means with phase shift

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Computer Graphics and Computer-Aided Design

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