Liu Caixia, Xu Zhenguo, Zhao Keyun, Xie Wanli
College of Intelligent Education, Jiangsu Normal University, Xuzhou, China.
Jiangsu Engineering Research Center of Educational Informationization, Xuzhou, China.
Heliyon. 2023 May 23;9(6):e16499. doi: 10.1016/j.heliyon.2023.e16499. eCollection 2023 Jun.
As an important human capital investment, education is an effective means to improve the comprehensive quality of people. Education expenditure is an important material guarantee for the development of educational undertakings. Education expenditure data is highly susceptible to numerous economic and social factors that complicate its nonlinear structure. In order to model the complex nonlinear problems of the system, this paper proposes a generalized conformable fractional-order nonlinear grey prediction model for the first time by analyzing the traditional time series-based modeling method in a nonlinear grey domain. The proposed model expands on the classical grey Bernoulli model by introducing the generalized conformable fractional accumulation as a new accumulation generator and utilizes error minimization principles in the modeling process. By altering the optimal order of the model and the cumulative generation operator, this model can adapt to various time series and reduce errors. Finally, the model is applied to education expenditure forecasting, and it is proved that the proposed model achieved good results and has higher accuracy than other models.
作为一项重要的人力资本投资，教育是提高人的综合素质的有效手段。教育支出是教育事业发展的重要物质保障。教育支出数据极易受到众多经济和社会因素的影响，这些因素使其非线性结构变得复杂。为了对系统的复杂非线性问题进行建模，本文首次通过在非线性灰色领域分析基于传统时间序列的建模方法，提出了一种广义一致分数阶非线性灰色预测模型。该模型通过引入广义一致分数累加作为新的累加生成器，对经典灰色伯努利模型进行了扩展，并在建模过程中运用了误差最小化原则。通过改变模型的最优阶数和累加生成算子，该模型能够适应各种时间序列并减少误差。最后，将该模型应用于教育支出预测，结果表明所提模型取得了良好的效果，且比其他模型具有更高的精度。
