Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and the Probabilities of Trends of Fuzzy Logical Relationships

Shyi‐Ming Chen · Shen-Wen Chen

135 citations0/0 files storedIEEE Transactions on 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 for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.

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References

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 2002FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES 2004Weighted fuzzy time series models for TAIEX forecasting 2000Temperature prediction using fuzzy time series

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