Improved NN-GM(1,1) for postgraduates' employment confidence index forecasting

Abstract

Postgraduates' employment confidence index (ECI) forecasting can help the university to predict the future trend of postgraduates' employment. However, the common forecast method based on the grey model (GM) has unsatisfactory performance to a certain extent. In order to forecast postgraduates' ECI efficiently, this paper discusses a novel hybrid forecast model using limited raw samples. Different from previous work, the residual modified GM(1,1) model is combined with the improved neural network (NN) in this work. In particullar, the hybrid model reduces the residue of the standard GM(1,1) model as well as accelerating the convergence rate of the standard NN. After numerical studies, the illustrative results are provided to demonstrate the forecast performance of the proposed model. In addition, some strategies for improving the postgraduates' employment confidence have been discussed.