A fuzzy time-series prediction by GA based rough sets model
Jing Zhao, Junzo Watada, Yoshiyuki Matsumoto · 2015
Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, Japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.