胡珍.基于L1范数的出国留学人数组合预测研究[J].重庆工商大学学报(自然科学版),2022,39(3):61-69
HU Zhen.Research on Combination Forecasting of the Number of Students Studying Abroad Based on L1 Norm[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2022,39(3):61-69
基于L1范数的出国留学人数组合预测研究
Research on Combination Forecasting of the Number of Students Studying Abroad Based on L1 Norm
  
DOI:
中文关键词:  GM(1,3)模型  BP神经网络模型  L1范数  组合预测  出国留学人数
英文关键词:GM(1,3) model  BP neural network model  L1 norm  combination forecasting  the number of students studying abroad
基金项目:
作者单位
胡珍 湖北工业大学理学院, 武汉 430068 
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中文摘要:
      为更准确地探究我国出国留学人数变化趋势,提出基于L1范数的组合预测模型,对出国留学人数进行预测;从多角度选取影响出国留学的因素,利用灰色关联度分析提取影响出国留学人数的典型因 子,进而构建 GM(1,3)模型;建立BP神经网络模型;提出基于L1范数组合预测模型,通过求解线性规划确定单一模型最优权系数;然后,对2006—2019年出国留学人数进行预测;选取GM(1,1)模型为对照模型,通过对照模型以及预测误差评价指标体系比较模型的预测精度,结果表明:基于L1范数的组合预测模型效果优于3个单一模型,有效地提高了预测精度,能够充分利用单一预测模型提供的信息,从而更加准确地预测出国留学人数;未来几年我国出国留学规模仍有较大的发展空间,预测结果可为全球疫情下我国留学相关工作提供参考。
英文摘要:
      In order to more accurately explore the trend of the number of people studying abroad in China, a combination forecasting model based on L1 norm is proposed to make a prediction about the number of people studying abroad. The factors that affect the number of students studying abroad are selected from multiple perspectives, and the typical factors that affect the number of students studying abroad are extracted by grey correlation analysis, and the GM(1,3) model is constructed. BP neural network model is established. Combination prediction model based on L1 norm is proposed, and the optimal weight coefficient of single model is determined by solving linear programming. Subsequently, it forecasts the number of students studying abroad from 2006 to 2019. The GM(1,1) model is selected as the control model, and the prediction accuracy of the model is compared through the control model and the prediction error evaluation index system. The results show that the combination forecasting model based on L1 norm outperforms three single models, which effectively improves the prediction accuracy and makes full use of the information provided by the single prediction model, thus more accurately predicting the number of students studying abroad. In the next few years, there is still a large space for the development of the scale of studying abroad in China. The prediction results can provide reference for the relevant work of studying abroad in China under the global epidemic situation.
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