Fuzzy forecasting based on fuzzy-trend logical relationship groups.
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
Chen
Shyi-Ming
SM
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan. smchen@mail.ntust.edu.tw
Wang
Nai-Yi
NY
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005544:Forecasting; D017143:Fuzzy Logic; D016015:Logistic Models; D017059:Models, Econometric; D010363:Pattern Recognition, Automated / Q000379:methods*; D013624:Taiwan