基于直觉模糊化的广义模糊时间序列预测模型

本文在传统广义模糊时间序列预测模型数据模糊化的基础上,引入直觉模糊集理论对其进行扩展。首先,在隶属度和非隶属度函数中增加犹豫度因子对样本数据进行直觉模糊化,更加细腻的反映数据不确定性本质。然后,用记分函数描述样本数据对模糊集的隶属情况,简化模型的复杂度。随后以传统广义模型为框架,构建基于直觉模糊化的广义模糊时间序列预测模型。最后利用典型的Alabama大学入学人数为实验数据,对比分析本文建立模型与传统广义模型的预测结果,验证直觉模糊化的广义模糊时间序列模型的可行性和优越性。

Generalized Fuzzy Time Series Model Based On Intuitionistic Fuzzy

This paper points out the deficiency of the traditional generalized fuzzy time series model in the sample data fuzzification,and introduces the theory of intuitionistic fuzzy sets to the traditional model.First of all,we increase a hesitant degree factor in the membership and the membership functions in order to make the sample data intuitionistic fuzzy,and it reflects the uncertainty of the actual data more exquisite.Then,we use score function to describe the membership in a simplified model.In the framework of traditional generalized model,it builds a generalized fuzzy time series model based on intuitionistic fuzzy.At last,the typical Alabama university enrollment is chosen as the forecasting targets,and the empirical results show that the proposed model greatly outperforms the conventional counterparts.