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\iteman{io-port 06017440}
\itemau{Hassan, Md.Rafiul; Nath, Baikunth; Kirley, Michael; Kamruzzaman, Joarder}
\itemti{A hybrid of multiobjective Evolutionary Algorithm and HMM-Fuzzy model for time series prediction.}
\itemso{Neurocomputing 81, 1-11 (2012).}
\itemab
Summary: 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.
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\itemcc{}
\itemut{fuzzy logic; Hidden Markov Model; time series; prediction methods}
\itemli{doi:10.1016/j.neucom.2011.09.012}
\end