Higher-order Markov chain models for categorical data sequences

In this paper we study higher‐order Markov chain models for analyzing categorical data sequences. We propose an efficient estimation method for the model parameters. Data sequences such as DNA and sales demand are used to illustrate the predicting power of our proposed models. In particular, we appl...

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Bibliographic Details
Published in:Naval research logistics 2004-06, Vol.51 (4), p.557-574
Main Authors: Ching, Wai Ki, Fung, Eric S., Ng, Michael K.
Format: Article
Language:English
Subjects:
categorical data
higher-order Markov model
linear programming
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Summary:In this paper we study higher‐order Markov chain models for analyzing categorical data sequences. We propose an efficient estimation method for the model parameters. Data sequences such as DNA and sales demand are used to illustrate the predicting power of our proposed models. In particular, we apply the developed higher‐order Markov chain model to the server logs data. The objective here is to model the users' behavior in accessing information and to predict their behavior in the future. Our tests are based on a realistic web log and our model shows an improvement in prediction. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004
ISSN:0894-069X
1520-6750
DOI:10.1002/nav.20017