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 language | English |
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Title of host publication | 2015 23rd European Signal Processing Conference, EUSIPCO 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2351-2355 |
Number of pages | 5 |
ISBN (Electronic) | 9780992862633 |
DOIs | |
Publication status | Published - 2015 Dec 22 |
Event | 23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France Duration: 2015 Aug 31 → 2015 Sept 4 |
Publication series
Name | 2015 23rd European Signal Processing Conference, EUSIPCO 2015 |
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Other
Other | 23rd European Signal Processing Conference, EUSIPCO 2015 |
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Country/Territory | France |
City | Nice |
Period | 15/8/31 → 15/9/4 |
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