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

DOI

10.2991/aeece-15.2015.91How to use a DOI?

Keywords

Container sea-rail transport volume; Grey theory; RBF neural network; Grey-RBF neural network

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huijun Wu
AU  - Guiyun Liu
PY  - 2015/09
DA  - 2015/09
TI  - Container Sea-Rail Transport Volume Forecasting of Ningbo Port Based on Combination Forecasting Model
BT  - Proceedings of the International Conference on Advances in Energy, Environment and Chemical Engineering
PB  - Atlantis Press
SP  - 449
EP  - 454
SN  - 2352-5401
UR  - https://doi.org/10.2991/aeece-15.2015.91
DO  - 10.2991/aeece-15.2015.91
ID  - Wu2015/09
ER  -