Forecasting education expenditure with a generalized conformable fractional-order nonlinear grey system model

Area of Science:

• Econometrics
• Time Series Analysis
• Fractional Calculus

Background:

• Education expenditure is crucial for human capital development.
• Education expenditure data exhibits complex nonlinear structures influenced by economic and social factors.
• Traditional time series models struggle with the nonlinear dynamics of education expenditure.

Purpose of the Study:

• To propose a novel generalized conformable fractional-order nonlinear grey prediction model.
• To enhance the modeling of complex nonlinear systems, specifically education expenditure.
• To improve the accuracy of education expenditure forecasting.

Main Methods:

• Developed a generalized conformable fractional-order nonlinear grey prediction model.
• Introduced generalized conformable fractional accumulation as a new accumulation generator.
• Utilized error minimization principles and optimized model order and cumulative generation operators.

Main Results:

• The proposed model effectively captures the nonlinear structure of education expenditure data.
• The model demonstrated adaptability to various time series by adjusting its order and operator.
• Achieved superior forecasting accuracy compared to existing models.

Conclusions:

• The generalized conformable fractional-order nonlinear grey prediction model is a powerful tool for education expenditure forecasting.
• This novel approach offers improved accuracy and adaptability for complex economic time series.
• The model provides a robust method for material guarantee in educational development planning.