Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets

This study proposes a hybrid fuzzy time series model with two advanced methods, cumulative probability distribution approach (CPDA) and rough set rule induction, to forecast stock markets. To improve forecasting accuracy, three refining processes of fuzzy time series are provided in the proposed model: (1) using CPDA to discretize the observations in training datasets based on the characteristics of data distribution, (2) generating rules (fuzzy logical relationships) by rough set algorithm and (3) producing forecasting results based on rule support values from rough set algorithm. To verify the forecasting performance of the proposed model in detail, two empirical stock markets (TAIEX and NYSE) are used as evaluating databases; two other methodologies, proposed by Chen and Yu, are used as comparison models, and two different evaluation methods (moving windows) are used. The proposed model shows a greatly improved performance in stock market forecasting compared to other fuzzy time series models.