Application of type-2 neuro-fuzzy modeling in stock price prediction

Authors:
Chih-Feng Liu;Chi-Yuan Yeh;Shie-Jue Lee
Affiliations:
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan
Venue:
Applied Soft Computing
Year:
2012

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Abstract

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base. Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares estimation. Experimental results, obtained by running on several datasets taken from TAIEX and NASDAQ, demonstrate the effectiveness of the type-2 neuro-fuzzy modeling approach in stock price prediction.