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. Attached Files Name Description MIMEType Size Downloads Title Faster and parameter-free discord search in quasi-periodic time series Author(s) 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: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) 2018 ERA Submission Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 14 May 2013, 13:50:45 EST Tue, 14 May 2013, 13:59:19 EST Thu, 16 May 2013, 15:11:15 EST Wed, 23 Oct 2013, 12:56:35 EST Fri, 25 Oct 2013, 08:48:53 EST Wed, 30 Oct 2013, 13:31:25 EST Fri, 10 Oct 2014, 09:05:33 EST Tue, 06 Sep 2016, 21:03:08 EST Sat, 21 Oct 2017, 01:40:20 EST Sat, 05 Dec 2020, 01:30:47 EST Filtered Full Citation counts:   Cited 8 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 579 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Tue, 14 May 2013, 13:50:45 EST

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