A Second-Order Fuzzy Time Series Model for Stock Price Analysis

doi:10.12068/j.issn.1005-3026.2019.02.028

Abstract

Abstract: It is difficult to model stock market because of its uncertainty， while fuzzy time series has its advantages in dealing with fuzzy and uncertainty data. Accordingly， the data was first preprocessed and a new way to divide the universe of discourse was given， after which the data was fuzzified using the triangular membership function， and a three-layer BP neural network was then established according to the fuzzified data. Finally， the generalized inverse fuzzy number formula was used to defuzzify the fuzzy relation， with the prediction results obtained. The method was used for predicting the stock price of State Bank of India(SBI)and the enrollment of the University of Alabama， and the results showed that the prediction accuracy is higher than that of the related previous methods.

Key words: second-order fuzzy time series, BP neural network, inverse fuzzy number, fuzzy time series, stock price

CLC Number:

O159

LIU Zhi， ZHANG Tie， DONG Ying， XU Shuang-shuang. A Second-Order Fuzzy Time Series Model for Stock Price Analysis[J]. Journal of Northeastern University Natural Science, 2019, 40(2): 300-304.

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