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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.

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Title Discovering semantics from multiple correlated time series stream
Author(s) Qiao, Zhi
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
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
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