TAIEX forecasting based on fuzzy time series and clustering techniques

Most of the fuzzy forecasting methods based on fuzzy time series used the static length of intervals, i.e., the same length of intervals. The drawback of the static length of intervals is that the historical data are roughly put into intervals, even if the variance of the historical data is not high. In this paper, we present a new method for forecasting the Taiwan stock exchange capitalization weighted stock index (TAIEX) based on fuzzy time series and clustering techniques. The proposed method gets higher forecasting accuracy rates than Chens´s method and Huarng et al.´s method to forecast the TAIEX.