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

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). (C) 2010 Elsevier B.V. All rights reserved.