A hybrid ANFIS model based on AR and volatility for TAIEX forecasting

Authors:
Jing-Rong Chang;Liang-Ying Wei;Ching-Hsue Cheng
Affiliations:
Department of Information Management, Chaoyang University of Technology 168, Jifong East Road, Wufong Township, Taichung County 41349, Taiwan;Department of Information Management, Yuanpei Institute of Science and Technology, No. 306, Yuanpei Street, Hsin Chou 300, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Touliu, Yunlin 640, Taiwan
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Time series models have been applied to forecast stock index movements and make reasonably accurate predictions. There are, however, two major drawbacks of conventional time series models: (1) most conventional time series models use only one variable to forecast; and (2) the rules that are mined from artificial neural networks (ANNs) are not easily understandable. To solve these problems and enhance the forecasting performance of fuzzy time series models, this paper proposes a hybrid adaptive network-based fuzzy inference system (ANFIS) model that is based on AR and volatility to forecast stock price problems of the Taiwan stock exchange capitalization weighted stock index (TAIEX). To evaluate forecasting performance, the proposed model is compared with Chen's model and Yu's model. Our results indicate that the proposed model is superior to other methods with regard to root mean squared error (RMSE).