Conference Proceedings

HMM based fuzzy model for time series prediction

Md Rafiul Hassan, Baikunth Nath, Michael Kirley

2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | IEEE | Published : 2006

Abstract

This paper presents a Hidden Markov Model (HMM) based fuzzy rule extraction technique for predicting a time series generated by a chaotic dynamical system. The model uses three sequential phases. Firstly, the HMM is used to partition the input dataset based on the ordering of the calculated log-likelihood values (similarity measures). Then, a recursive top-down algorithm is used to generate the minimum number of rules required to accurately predict the next value in the time series using the training dataset. Finally, a gradient descent method is applied to the extracted fuzzy rules in order to fine-tune the model parameters. The performance of the proposed model is evaluated using a benchma..

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