Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

被引:3
作者
Wu, Geng [1 ]
Fu, Haiwei [1 ]
Jiang, Peng [2 ]
Chi, Rui [2 ]
Cai, Rongjiang [3 ]
机构
[1] Ningbo Univ Technol, Sch Econ & Management, Ningbo 315211, Peoples R China
[2] Shandong Univ, Sch Business, Weihai 264209, Peoples R China
[3] Macao Polytech Univ, Fac Humanities & Social Sci, Taipa 999078, Macao, Peoples R China
关键词
overseas talent; grey model; dynamic fractional order; GWO; MLP; CONSUMPTION;
D O I
10.3390/fractalfract8040217
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
International students play a crucial role in China's talent development strategy. Thus, predicting overseas talent mobility is essential for formulating scientifically reasonable talent introduction policies, optimizing talent cultivation systems, and fostering international talent cooperation. In this study, we proposed a novel fractional-order grey model based on the Multi-Layer Perceptron (MLP) and Grey Wolf Optimizer (GWO) algorithm to forecast the movement of overseas talent, namely MGDFGM(1,1). Compared to the traditional grey model FGM(1,1), which utilizes the same fractional order at all time points, the proposed MGDFGM(1,1) model dynamically adjusts the fractional-order values based on the time point. This dynamic adjustment enables our model to better capture the changing trends in the data, thereby enhancing the model's fitting capability. To validate the effectiveness of the MGDFGM(1,1) model, we primarily utilize Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as the evaluation criteria for the prediction accuracy, as well as standard deviation (STD) as an indicator of the model stability. Furthermore, we perform experimental analysis to evaluate the predictive performance of the MGDFGM(1,1) model in comparison to NA & Iuml;VE, ARIMA, GM(1,1), FGM(1,1), LSSVR, MLP, and LSTM. The research findings demonstrate that the MGDFGM(1,1) model achieves a remarkably high level of prediction accuracy and stability for forecasting overseas talent mobility in China. The implications of this study offer valuable insights and assistance to government departments involved in overseas talent management.
引用
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页数:17
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