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Fuzzy forecasting based on fuzzy-trend logical relationship groups.
- Source :
-
IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society [IEEE Trans Syst Man Cybern B Cybern] 2010 Oct; Vol. 40 (5), pp. 1343-58. Date of Electronic Publication: 2010 Jan 15. - Publication Year :
- 2010
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 1941-0492
- Volume :
- 40
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
- Publication Type :
- Academic Journal
- Accession number :
- 20083457
- Full Text :
- https://doi.org/10.1109/TSMCB.2009.2038358