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A Combined Forecasting Method of College Employment Population with Differential Evolution Algorithm

Xinjuan Zhang

Abstract



Along with the accelerated pace of socio-economic development in China, the college employment issue as a hot topic arouses extensive attention of all walks of life. The prediction result serves for providing valuable information, which is conductive to the establishment of highly suitable relationship among enterprises and colleges. In this article, a combined forecasting model with differential evolution optimizing weights of college employment population is carried out. The proposed model can improve the performance of each single forecasting model such as regression, BPNN or SVM. For sake of proving the effectiveness of the model mentioned in this paper, an application concerning the population of China's college graduates is presented. All of empirical results come to the conclusion that the proposed model obtains the maximum value of mean absolute percentage error (MAPE) and is superior to the corresponding result estimated with regression, BPNN or SVM.

Keywords


college employment population, combined forecasting model, differential evolution algorithm.

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