Fuzzy Forecasting Based on Fuzzy-Trend Logical Relationship Groups

Shyi‐Ming Chen · Naiyi Wang

136 citations0/0 files storedIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)

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Stock Market Forecasting Methods Forecasting Techniques and Applications Fuzzy Logic and Control Systems

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.

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1965Fuzzy sets 1996Fuzzy sets 1993Forecasting enrollments with fuzzy time series — Part I 1996Forecasting enrollments based on fuzzy time series 1994Forecasting enrollments with fuzzy time series — part II 2001Effective lengths of intervals to improve forecasting in fuzzy time series 2002FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES 2004Weighted fuzzy time series models for TAIEX forecasting

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2014Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and the Probabilities of Trends of Fuzzy Logical Relationships 2012Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and Particle Swarm Optimization Techniques 2012TAIEX Forecasting Using Fuzzy Time Series and Automatically Generated Weights of Multiple Factors 2016Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques 2019Fuzzy time series forecasting based on proportions of intervals and particle swarm optimization techniques

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