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General Information
    • ISSN: 2010-3719 (Online)
    • Abbreviated Title: Int. J. Inf. Electron. Eng.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIEE
    • Editor-in-Chief: Prof. Chandratilak De Silva Liyanage
    • Executive Editor: Jennifer Zeng
    • Abstracting/ Indexing : Google Scholar, Electronic Journals Library, Crossref and ProQuest, Ei (INSPEC, IET), EBSCO.
    • E-mail ijiee@ejournal.net

University of Brunei Darussalam, Brunei Darussalam   
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IJIEE 2017 Vol.7(1): 34-40 ISSN: 2010-3719
DOI: 10.18178/IJIEE.2017.7.1.658

Evolutions Detection Using Markov Models

Arnaud Paris, Adnen El-Amraoui, Nathalie Cislo, and Selma Arbaouiand NacimRamdani
Abstract—We consider a specific case of the anomaly detection problem where the considered system is unknown but observable. The aim is to detect if this system changes or not for a long period, and, if possible, to characterize these evolutions. The proposed method consists of two steps. The first one is to learn the system thanks to a Hidden Markov Model with explicit duration of states, using observations of the system at different moments. The second step is to compute distances between these models to detect the evolutions. Due to the constraints of the learning algorithm and computational time, we proposed a new method to compute distances between Markov models, based on the Kullback-Leibler divergences. The method is tested on a trial evolving model. These results are discussed and limits of the method are highlighted.

Index Terms—Evolution detection, distance measure, markov models, model comparison.

The authors are with the Univ. Orléans, INSA-CVL, PRISME EA 4229, F45072, Orléans, France (e-mail: arnaud.paris1@univ-orleans.fr).


Cite:Arnaud Paris, Adnen El-Amraoui, Nathalie Cislo, and Selma Arbaouiand NacimRamdani, "Evolutions Detection Using Markov Models," International Journal of Information and Electronics Engineering vol. 7, no. 1, pp. 34-40, 2017.

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