Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures

作     者：Cheng, Shou-Hsiung Chen, Shyi-Ming Jian, Wen-Shan 

作者机构：Chien Kuo Technol Univ Dept Informat Management Changhua Taiwan Chien Kuo Technol Univ Dept Kinesiol Hlth Leisure Studies Changhua Taiwan Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2016年第327卷

页      面：272-287页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Chienkuo Technology University, Changhua, Taiwan [CTU-103-RP-KH-001-019-A] Ministry of Science and Technology, Republic of China [MOST 104-2221-E-011-084-MY3] 

主　　题：Fuzzy logical relationships Fuzzy time series K-means clustering algorithm Particle swarm optimization Similarity measures 

摘      要：In this paper, we propose a new fuzzy time series forecasting method for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series, fuzzy logical relationships, particle swarm optimization techniques, the K-means clustering algorithm, and similarity measures between the subscript of the fuzzy set of the fuzzified historical testing datum on the previous trading day and the subscripts of the fuzzy sets appearing in the current states of the fuzzy logical relationships in the chosen fuzzy logical relationship group. The particle swarm optimization techniques are used to get the optimal partition of the intervals in the universe of discourse. The K-means clustering algorithm is used to cluster the subscripts of the fuzzy sets of the current states of the fuzzy logical relationships to get the cluster center of each cluster and to divide the constructed fuzzy logical relationships into fuzzy logical relationship groups. The experimental results show that the proposed fuzzy forecasting method gets higher forecasting accuracy rates than the existing methods. The advantages of the proposed fuzzy forecasting method is that it uses the particle swarm optimization techniques to get the optimal partition of the intervals in the universe of discourse and uses the K-means clustering algorithm to cluster the subscripts of the fuzzy sets of the current states of the fuzzy logical relationships to get the cluster center of each cluster and to divide the constructed fuzzy logical relationships into fuzzy logical relationship groups for increasing the forecasting accuracy rates. (C) 2015 Elsevier Inc. All rights reserved.