An Improved the Prediction Accuracy of the Nonlinear Grey Bernoulli Model by Fourier Series and Its Application in Container Throughput Forecasting in Danang Port

Abstract

In order to improve the prediction accuracy of Nonlinear Grey Bernoulli Model NGBM (1,1), this study using Fourier series to modify their residual error of this model. To verify the effectiveness of the proposed approach, the annual water consumption in Wuhan from 2005 to 2012 is used for the modeling to forecast the annual water consumption demand from 2013 to May 2020, and the forecasting results proved that the Fourier- NGBM (1,1) is a better than the among forecasting model used in this situation. Furthermore, this proposed approach is applied the real case in forecasting the Container Throughput Forecasting in Danang Port. The empirical results show that the proposed model will get a higher accuracy performance with the lowest MAPE =1.93%. This result is not only show the effectiveness of proposed model but also offers valuable insights for Danang policymakers in orientation and planning management agency so as to boost the development of upcoming port activities.