An extensive conformable fractional grey model and its application

被引:2
|
作者
Xu, Jie [1 ]
Wu, Wen-Ze [2 ]
Liu, Chong [3 ]
Xie, Wanli [4 ]
Zhang, Tao [5 ]
机构
[1] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China
[2] Jiangsu Univ, Sch Math Sci, Zhenjiang 212013, Peoples R China
[3] Northeastern Univ, Coll Sci, Shenyang 110819, Peoples R China
[4] Qufu Normal Univ, Sch Commun, Rizhao 276826, Peoples R China
[5] Guangxi Univ Sci & Technol, TUS Coll Digit, Liuzhou 545006, Peoples R China
基金
中国国家自然科学基金;
关键词
Grey prediction; Conformable fractional accumulation; Monte Carlo simulation; China's primary energy consumption; ENERGY-CONSUMPTION; FORECASTING-MODEL; SYSTEM MODEL; ARIMA;
D O I
10.1016/j.chaos.2024.114746
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In order to expand the applicability of the conventional conformable fractional grey model, an extensive conformable fractional grey model, abbreviated as ECFGM(1,1), is proposed by the introduction of the extensive conformable fractional accumulation. Specifically, the improvements of the proposed model can be outlined as follows. First, the extensive fractional accumulation and difference are designed for accumulated generating operation and its reverse calculation, respectively. Second, based on the extensive form, the parameter estimation and time response function of the ECFGM(1,1) model are deduced, thereinto, the particle swarm optimization algorithm is employed to determine the optimal fractional order for the newly -designed model. It is worthy noting that an algorithm framework by the Monte Carlo simulation and parameter sensitivity analysis is conducted to assess the robustness of the proposed model. To validate this model's efficacy, the novel technique is adopted to forecast China's primary energy consumption compared with a series of competitive models. The numerical results indicate the newly -proposed model is superior to all competitors in terms of MAPE and RMSE values, thus, the proposed ECFGM(1,1) model is considered a powerful and promising method for enhancing the existing fractional grey models.
引用
收藏
页数:10
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