title: Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques.
abstract: 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.affiliation: Department of Computer Science and Information Engineering, National Taiwan University of Scienceand Technology, Taipei 106, Taiwan. smchen@mail.ntust.edu.tw
authors.forename: Shyi-Ming
authors.initials: SM
authors.lastname: Chen
authors.forename: Gandhi Maruli Tua
authors.initials: GM
authors.lastname: Manalu
authors.forename: Jeng-Shyang
authors.initials: JS
authors.lastname: Pan
authors.forename: Hsiang-Chuan
authors.initials: HC
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005544:Forecasting; D016015:Logistic Models; D010363:Pattern Recognition, Automated / Q000379:methods*