Adaptive Anomaly Detection of Coupled Activity Sequences

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dc.contributor.author Ou, Yuming en_US
dc.contributor.author Cao, Longbing en_US
dc.contributor.author Zhang, Chengqi en_US
dc.contributor.editor en_US
dc.date.accessioned 2010-05-28T09:47:12Z
dc.date.available 2010-05-28T09:47:12Z
dc.date.issued 2009 en_US
dc.identifier 2009001907 en_US
dc.identifier.citation Ou Yuming, Cao Longbing, and Zhang Chengqi 2009, 'Adaptive Anomaly Detection of Coupled Activity Sequences', IEEE, vol. 10, no. 1, pp. 12-16. en_US
dc.identifier.issn 1727-5997 en_US
dc.identifier.other C1 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.language en_US
dc.publisher IEEE en_US
dc.relation.isbasedon en_US
dc.title Adaptive Anomaly Detection of Coupled Activity Sequences en_US
dc.parent The IEEE Intelligent Informatics Bulletin en_US
dc.journal.volume 10 en_US
dc.journal.number 1 en_US
dc.publocation United States of America en_US
dc.identifier.startpage 12 en_US
dc.identifier.endpage 16 en_US
dc.cauo.name FEIT.A/DRsch Ctr Quantum Computat'n & Intelligent Systs en_US
dc.conference Verified OK en_US
dc.for 080600 en_US
dc.personcode 999551 en_US
dc.personcode 034535 en_US
dc.personcode 011221 en_US
dc.percentage 50 en_US
dc.classification.name Information Systems en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Multiple coupled sequences, Anomaly, HMM, Adaptation, Stock market. en_US
dc.staffid en_US
dc.staffid 011221 en_US


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