Container Sea-Rail Transport Volume Forecasting of Ningbo Port Based on Combination Forecasting Model

摘要

According to Grey theory and Radial Basis Function (Radial Basis Function, RBF) neural network forecasting method respective characteristics, based on Ningbo port container sea-rail transport volume for the original data in recent six years, Grey-RBF neural network combined forecasting model is used to predict container sea-rail transport volume development trend in the coming two years. Prediction results show that the average relative error of Grey-RBF neural network combined forecasting model predicted value and the actual value is minimum, the fitting accuracy is higher than the single GM (1, 1) model and RBF neural network model.