Adaptive Anomaly Detection of Coupled Activity Sequences

Ou, Y; Cao, L; Zhang, C

Adaptive Anomaly Detection of Coupled Activity Sequences

Ou, Y Cao, L

Zhang, C

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Publisher:: IEEE
Publication Type:: Journal Article
Citation:: The IEEE Intelligent Informatics Bulletin, 2009, 10 (1), pp. 12 - 16
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	The IEEE Intelligent Informatics Bulletin, 2009, 10 (1), pp. 12 - 16	en_US
dc.identifier.issn	1727-5997	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9058
dc.description.abstract	Many real-life applications often involve multiple sequences, which are coupled with each other. It is unreasonable to either study the multiple coupled sequences separately or simply merge them into one sequence, because the information about their interacting relationships would be lost. Furthermore, such coupled sequences also have frequently significant changes which are likely to degrade the performance of trained model. Taking the detection of abnormal trading activity patterns in stock markets as an example, this paper proposes a Hidden Markov Model-based approach to address the above two issues. Our approach is suitable for sequence analysis on multiple coupled sequences and can adapt to the significant sequence changes automatically. Substantial experiments conducted on a real dataset show that our approach is effective.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	The IEEE Intelligent Informatics Bulletin	en_US
dc.rights	© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Adaptive Anomaly Detection of Coupled Activity Sequences	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	10	en_US
utslib.for	0807 Library and Information Studies	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	1	en_US
pubs.volume	10	en_US

Abstract:

Many real-life applications often involve multiple sequences, which are coupled with each other. It is unreasonable to either study the multiple coupled sequences separately or simply merge them into one sequence, because the information about their interacting relationships would be lost. Furthermore, such coupled sequences also have frequently significant changes which are likely to degrade the performance of trained model. Taking the detection of abnormal trading activity patterns in stock markets as an example, this paper proposes a Hidden Markov Model-based approach to address the above two issues. Our approach is suitable for sequence analysis on multiple coupled sequences and can adapt to the significant sequence changes automatically. Substantial experiments conducted on a real dataset show that our approach is effective.

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http://hdl.handle.net/10453/9058