A hybrid of multiobjective Evolutionary Algorithm and HMM-Fuzzy model for time series prediction

Authors:
Md. Rafiul Hassan;Baikunth Nath;Michael Kirley;Joarder Kamruzzaman
Affiliations:
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia;Gippsland School of IT, Monash University, Churchill, VIC 3842, Australia
Venue:
Neurocomputing
Year:
2012

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Abstract

In this paper, we introduce a new hybrid of Hidden Markov Model (HMM), Fuzzy Logic and multiobjective Evolutionary Algorithm (EA) for building a fuzzy model to predict non-linear time series data. In this hybrid approach, the HMM's log-likelihood score for each data pattern is used to rank the data and fuzzy rules are generated using the ranked data. We use multiobjective EA to find a range of trade-off solutions between the number of fuzzy rules and the prediction accuracy. The model is tested on a number of benchmark and more recent financial time series data. The experimental results clearly demonstrate that our model is able to generate a reduced number of fuzzy rules with similar (and in some cases better) performance compared with typical data driven fuzzy models reported in the literature.