| Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and Particle Swarm Optimization Techniques. | |
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MedLine Citation:
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PMID: 23193240 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Shyi-Ming Chen; Gandhi Maruli Tua Manalu; Jeng-Shyang Pan; Hsiang-Chuan Liu |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-11-10 |
Journal Detail:
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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:
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Created Date: 2012-11-29 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9890044 Medline TA: IEEE Trans Syst Man Cybern B Cybern Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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