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

被引：0
作者：
Liu Z. [1 ,2 ]
Zhang T. [1 ]
Dong Y. [3 ]
Xu S.-S. [2 ]
机构：
[1] School of Sciences, Northeastern University, Shenyang
[2] Department of Basics, Shenyang University of Technology, Liaoyang
[3] College of Science, Dalian Nationalities University, Dalian
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2019年 / 40卷 / 02期
关键词：
BP neural network; Fuzzy time series; Inverse fuzzy number; Second-order fuzzy time series; Stock price;
D O I：
10.12068/j.issn.1005-3026.2019.02.028
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：300 / 304
页数：4
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