High-order fuzzy time series based on rough set for forecasting TAIEX

Time series data consists of consecutive observations with continuous value changing in time. The data itself implies a fluctuation characteristic which can be forecasted. But different periods of stock prices results from non-equivalent data generating models. Therefore, this study presents a high-order fuzzy time-series method based on rough set for forecasting a ten-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index). In empirical analysis, this study takes 4 different lag periods (t-1, t-2, t-3, and t-4), which mean different orders, from I-order to 4-order, as the input attributes to evaluate the proposed method and compares the forecasting results with those derived from Chen's (1996) and Yu's (2004) methods. The experimental results show that the best performance for the proposed method is fall on I-order and the proposed method outperforms Yu's and Chen's in the all testing periods except 1997.