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Faster and parameter-free discord search in quasi-periodic time series

Luo, Wei and Gallagher, Marcus 2011, Faster and parameter-free discord search in quasi-periodic time series, in PAKDD 2011 : Advances in Knowledge Discovery and Data Mining : Proceedings of PAKDD 2011, Springer, Heidelberg, Germany, pp. 135-148, doi: 10.1007/978-3-642-20847-8_12.

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Title Faster and parameter-free discord search in quasi-periodic time series
Author(s) Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Gallagher, Marcus
Conference name Pacific-Asia Conference on Knowledge Discovery and Data Mining (15th : 2011 : Shenzhen, China)
Conference location Shenzhen, China
Conference dates 24-27 May. 2011
Title of proceedings PAKDD 2011 : Advances in Knowledge Discovery and Data Mining : Proceedings of PAKDD 2011
Editor(s) Huang, Joshua Zhexue
Cao, Longbing
Srivastava, Jaideep
Publication date 2011
Conference series Pacific-Asia Conference on Knowledge Discovery and Data Mining
Start page 135
End page 148
Total pages 14
Publisher Springer
Place of publication Heidelberg, Germany
Keyword(s) time series discord
minimax search
time series data mining
anomaly detection
periodic time series
Summary Time series discord has proven to be a useful concept for time-series anomaly identification. To search for discords, various algorithms have been developed. Most of these algorithms rely on pre-building an index (such as a trie) for subsequences. Users of these algorithms are typically required to choose optimal values for word-length and/or alphabet-size parameters of the index, which are not intuitive. In this paper, we propose an algorithm to directly search for the top-K discords, without the requirement of building an index or tuning external parameters. The algorithm exploits quasi-periodicity present in many time series. For quasi-periodic time series, the algorithm gains significant speedup by reducing the number of calls to the distance function.
ISBN 9783642283192
Language eng
DOI 10.1007/978-3-642-20847-8_12
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30052496

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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