广义模糊时间序列模型模糊区间划分研究

摘要

In the framework of fuzzy time series model ,the generalized fuzzy time series model and some methods for partitioning fuzzy interval are presented and summarized . Secondly , three generalized fuzzy time series models on the basis of average partition ,fuzzy C-mean (FCM ) clustering and automatic clustering techniques are presented .Enrollment of the University of Alabama and the close prices of Shanghai Stock Exchange Composite Index (SSECI) are served as the training data sets for the proposed models .The empirical analyses not only reveal the impact of three partition methods on the forecast results , but also show that the generalized model outperforms the conventional counterparts .%　　在模糊时间序列模型的构架中，介绍了广义模糊时间序列模型建立过程和常用的模糊区间划分方法，提出了基于均匀划分、模糊 C均值聚类和自动聚类3种模糊区间划分方法的广义模糊时间序列模型，并用Alabama大学入学人数和沪市股指两组数据对模型进行了详细的分析。实验结果不仅揭示了这3种方法对模型预测结果的影响，还证明了广义模型优于传统模型。