Research on combination forecast of port container throughput based on Elman neural network

Abstract

As a nonlinear variation, the port container throughput is easy to get affected by market. There are many prediction methods such as regression analysis, exponential smoothing. However, each of these prediction methods has their own characters which all lead to low precision of container throughput forecasting. Constructed on the foundation of BP network, the combination forecast model performs well in time series forecasting and solves the problem excellently. Consequently, based on Elman neural network, this article builds a combination forecast model to improve the precision level of forecast. At last, with the empirical analysis of container throughput in Shanghai port, the reliability and accuracy of the combination model are proved.