Publication
Title
Container throughput time series forecasting using a hybrid approach
Author
Abstract
This paper proposed a novel two-stage hybrid container throughput forecasting model. Time series in reality exhibits both linear and nonlinear characteristics and individual models are not able to describe the two features simultaneously. Therefore, we combine linear model SARIMA (seasonal autoregressive integrated moving average) and nonlinear model ANN (artificial neural network). In order to break through the limitations of traditional hybrid models, based on the identified parameters of SARIMA in first stage, the structures of several ANN in second stage could be decided. Finally, we validate the proposed hybrid model 5 performs best with case study in Shanghai port.
Language
English
Source (book)
Proceedings of the 2015 Chinese Intelligent Systems Conference, October 17-18, 2015, Yangzhou, China / Jia, Yingmin
Source (series)
Lecture notes in electrical engineering ; 359
Publication
Berlin : Springer, 2016
ISBN
978-3-662-48384-8
Volume/pages
p. 639-650
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
Record
Identification
Creation 23.09.2016
Last edited 29.09.2016