Discrete Grey DGMFP(1,1,r) Model with Fractional Polynomial and Its Application

被引:0
|
作者
Zhang, Jun [1 ]
Liu, Chong [1 ]
Lao, Tongfei [1 ]
Chen, Zhanbo [2 ]
机构
[1] Inner Mongolia Agr Univ, Coll Sci, Hohhot 010018, Peoples R China
[2] Guangxi Univ Finance & Econ, Sch Informat & Stat, Nanning 530007, Peoples R China
来源
JOURNAL OF GREY SYSTEM | 2021年 / 33卷 / 03期
基金
中国国家自然科学基金;
关键词
Discrete Grey Model; Quantum Genetic Algorithm; Prediction; Accuracy; FORECASTING-MODEL; CHINA;
D O I
暂无
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Discretization is an effective tactic to improve the accuracy of grey prediction model. In order to further improve the accuracy of the discrete grey prediction model, based on the discrete grey DGMP(1,1,N) model with polynomial, the degree of polynomial is expanded from integer to fraction, and the discrete grey DGMFP(1,1,r) model with fractional polynomial is proposed in the present study. To determine the best DGMFP(1,1,r) model, the mean absolute percentage error (MAPE) is established as an objective function of the optimization model, and a quantum genetic algorithm is used to calculate the optimal degree of fractional polynomials in DGMFP(1,1,r) model. Finally, the empirical results from two application cases indicate that, compared with other discrete grey models, DGMFP(1,1,r) model has a higher simulation and prediction accuracy and can overcome the restrictions of DGMP(1,1,N) model class ratio test, and has stronger generalization ability and wider adaptability.
引用
收藏
页码:31 / 42
页数:12
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