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