Discovering semantics from multiple correlated time series stream Qiao, Zhi, Huang, Guangyan, He, Jing, Zhang, Peng, Guo, Li, Cao, Jie and Zhang, Yanchun 2013, Discovering semantics from multiple correlated time series stream, in 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II, Springer, Berlin, Germany, pp. 509-520, doi: 10.1007/978-3-642-37456-2_43. Attached Files Name Description MIMEType Size Downloads Title Discovering semantics from multiple correlated time series stream Author(s) Qiao, Zhi Huang, Guangyan He, Jing Zhang, Peng Guo, Li Cao, Jie Zhang, Yanchun Conference name Pacific-Asia Conference on Knowledge Discovery and Data Mining (17th ; 2013 : Gold Coast, Qld.) Conference location Gold Coast, Qld. Conference dates 14-17 Apr. 2013 Title of proceedings 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II Publication date 2013 Series Lecture Notes in Computer Science v.7819 Start page 509 End page 520 Total pages 12 Publisher Springer Place of publication Berlin, Germany Summary In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and observations in multiple correlated time series to learn data generating rules. The transforming rules are learned from corresponding latent state sequence of multiple time series based on Markov chain character. The reusable semantics learned by CfSLF can be fed into various analysis tools, such as prediction or anomaly detection. Moreover, we present two algorithms based on the semantics, which can later be applied to next-n step prediction and anomaly detection. Experiments on real world data sets demonstrate the efficiency and effectiveness of the proposed method. © Springer-Verlag 2013. ISBN 9783642374555 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-642-37456-2_43 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 ERA Research output type E Conference publication Copyright notice ©2013, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30083697 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version 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 Wed, 25 May 2016, 12:58:40 EST Thu, 26 May 2016, 05:01:57 EST Mon, 30 May 2016, 14:43:23 EST Tue, 31 May 2016, 06:08:36 EST Fri, 10 Jun 2016, 12:07:03 EST Tue, 14 Jun 2016, 10:20:28 EST Thu, 16 Jun 2016, 10:40:42 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 75 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 30 May 2016, 14:43:23 EST

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