Document Detail


Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and Particle Swarm Optimization Techniques.
MedLine Citation:
PMID:  23193240     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.
Authors:
Shyi-Ming Chen; Gandhi Maruli Tua Manalu; Jeng-Shyang Pan; Hsiang-Chuan Liu
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-10
Journal Detail:
Title:  IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society     Volume:  -     ISSN:  1941-0492     ISO Abbreviation:  IEEE Trans Syst Man Cybern B Cybern     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9890044     Medline TA:  IEEE Trans Syst Man Cybern B Cybern     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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