A simple sequential outlier detection with several residuals

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Outlier detection schemes have been used to identify the unwanted noise and this helps us to obtain underlying valuable signals and predicting the next state of the systems/signals. However, there are few researches on sequential outlier detection in time series although a lot of outlier detection algorithms are developed in off-line systems. In this paper, we focus on the sequential (on-line) outlier detection schemes, that are based on the 'delete-replace' approach. We also demonstrate that three different types of residuals can be used to design the outlier detection scheme to achieve accurate sequential estimation: marginal residual, conditional residual, and contribution.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2351-2355
Number of pages5
ISBN (Electronic)9780992862633
DOIs
Publication statusPublished - 2015 Dec 22
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: 2015 Aug 312015 Sept 4

Publication series

Name2015 23rd European Signal Processing Conference, EUSIPCO 2015

Other

Other23rd European Signal Processing Conference, EUSIPCO 2015
Country/TerritoryFrance
CityNice
Period15/8/3115/9/4

Bibliographical note

Funding Information:
This research is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A1012797)

Publisher Copyright:
© 2015 EURASIP.

Keywords

  • Conditional residual
  • Contribution
  • Marginal residual
  • Outlier detection

ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

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