Set of Fuzzy Time Series Forecasting Models Based on the Difference Rate

被引：0

作者：

Zhu, Xiaojing ^{[1
]}

Wang, Hongxu ^{[1
]}

Yin, Chengguo ^{[1
]}

Lu, Xiaoli ^{[1
]}

机构：

[1] Hainan Trop Ocean Univ, Sanya, Hainan, Peoples R China

来源：

PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MODELLING, SIMULATION AND APPLIED MATHEMATICS (MSAM2017) | 2017年 / 132卷

关键词：

fuzzy time series forecasting method; SFBDR fuzzy number function; SFBDR inverse fuzzy number function; SFBDR Predicted function; ENROLLMENTS;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Song & Chissom introduced the concept of fuzzy time series in 1993[1], and many fuzzy time series methods have been proposed, however, the prediction accuracy is not high, among which, Jilani, Burney and Ardil (2007) proposed prediction model has achieved a high accuracy. This paper improves their predicted model, and proposed the set of fuzzy time series forecasting models Based on the difference rate, simplified as SFBDR, it contains the predicted model SFBDR (0.000001, 0.000003) and SFBDR (0.000003, 0.000001), in the historical enrollment of University of Alabama it can get the highest predicted accuracy of AFER= 0% and MSE=0.

引用

页码：49 / 52

页数：4

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