Forecast and Analysis of Reasons for Changes in the Number of Students Studying Abroad
DOI:
https://doi.org/10.54097/hset.v24i.3901Keywords:
Study Abroad; Multiple Regression; Machine Learning; Time Series; Gray Model; Combined Model.Abstract
Since 2000, the number of students studying abroad in China has been growing continuously. Based on the macro data of National Bureau of Statistics from 2005 to 2021, this paper analyzes the degree of influence of variables such as economy, epidemic, and policies on the number of students studying abroad, and constructs a GM (1, 1) model using SPSSAU to calculate the predicted values of variables for the next two years, and Multiple linear regression models, machine learning models, and time series models were constructed using Python, Eviews, and SPSS software to predict the future trend of the number of study abroad students; and based on this, a combination model based on weight assignment was constructed. The research results prove that the combined model has higher accuracy than the single model.
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