Forecasting the TAIEX based on fuzzy time series, PSO techniques and support vector machines

Authors:
Shyi-Ming Chen;Pei-Yuan Kao
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2013

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Abstract

This paper presents a new method for forecasting the TAIEX based on fuzzy time series, particle swarm optimization techniques and support vector machines. The proposed method to forecast the TAIEX is based on slope of one-day variations of the TAIEX and the slope of two-days average variations of the TAIEX. The particle swarm optimization techniques are used to get optimal intervals in the universe of discourse. The support vector machine is used to classify the training data set. The experimental results show that the proposed method outperforms the existing methods for forecasting the TAIEX.