An optimized conformable fractional non-homogeneous gray model and its application

Abstract

Developing a robust, accurate forecasting model and improving the prediction abilities of the limited historical data that lacks statistical rules has become a top priority. To address this problem, an improved conformable fractional non-homogeneous gray model, namely CFONGM(1,1,k,c), is proposed. Combining the dynamic background-value and particle swarm optimization algorithm to further improve forecasting ability of the existing gray model. In which matrix perturbation theory is employed to prove that the novel model conforms the principle of new information priority and has a smaller perturbation bound of solution. The two empirical examples of educational funds and new students enrollment of regular institutions of higher education of China are employed to examine the prediction accuracy of the novel model. The results show that the novel model has a better prediction performance compared with other competitive models.

KEYWORDS:

• Conformable fractional accumulation
• Gray model
• CFONGM(1,1,k,c)
• Particle swarm optimization

Data availability

The data used to support the findings of this study are included within the article.

Disclosure statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Additional information

Funding

Tao Zhang’s research was supported by the National Natural Science Foundation of China (Grant nos. 11861014, 11561006) and Natural Science Foundation of Guangxi (Grant no. 2018JJA110013); Wen-Ze Wu’s research was supported by the Fundamental Research Funds for the Central Universities of China (Grant no. 2019YBZZ062).