关于股票价格的二阶模糊时间序列

由于股票价格的时间序列具有不确定性，股市的真实模型不容易建立，而模糊时间序列在解决模糊性数据和不确定性数据方面具有较大优势;因此，本文首先将数据进行预处理并改进论域划分的方法，然后利用三角隶属度函数进行数据的模糊化处理，再利用模糊化后的数据建立三层BP神经网络，最后，应用广义的逆模糊数公式将预测模糊集进行逆模糊化，从而得到预测结果.应用本文方法对印度国家银行(SBI)股票价格和Alabama 大学的入学人数进行预测，预测结果精度较高.

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

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.