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Higher‐order Markov chain models for categorical data sequences *
Author(s) -
Ching Wai Ki,
Fung Eric S.,
Ng Michael K.
Publication year - 2004
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.20017
Subject(s) - categorical variable , markov chain , computer science , markov model , data mining , order (exchange) , variable order markov model , clickstream , machine learning , web server , the internet , world wide web , finance , web api , economics
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