Prediction of the Scale of Graduate Education in China Using a Combined Weighting GM(1,1) Model
Year: 2024
Type: conference-paper
Abstract: Accurately predicting the scale of graduate education in China holds practical significance for formulating reasonable educational policies. The GM(1,1) model performs better in forecasting uncertain systems with "small data and limited information." However, the model has drawbacks, such as excessive reliance on the smoothness of data sequences, background values, and parameter estimation methods, which can lead to significant prediction errors. Therefore, in this study, we first improve the smoothness of the data sequence using a function transformation method, and then apply the traditional GM(1,1) model, the unbiased GM(1,1) model, the background value-optimized GM(1,1) model, and the combined weighting GM(1,1) model to predict the scale of graduate education in China. The prediction performance of these models is compared, and the best-performing combined weighting model is selected to forecast the scale of graduate education in China from 2023 to 2026.
Authors: Cijun Fang, Jun Zhang, Bingxue Cui, Yuxin Wang, Tingting Wu
Institution: Hubei University of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4409965581