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Observer Design for Discrete-Time Switched Lipschitz Nonlinear Singular Systems with Time Delays and Unknown Inputs Read chapter Read first chapter

2016 | OriginalPaper | Chapter

65. Container Throughput Time Series Forecasting Using a Hybrid Approach

Authors : Xi Zha, Yi Chai, Frank Witlox, Le Ma

Published in: Proceedings of the 2015 Chinese Intelligent Systems Conference

Publisher: Springer Berlin Heidelberg

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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.

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Metadata
Title
Container Throughput Time Series Forecasting Using a Hybrid Approach
Authors
Xi Zha
Yi Chai
Frank Witlox
Le Ma
Copyright Year
2016
Publisher
Springer Berlin Heidelberg
Book
Proceedings of the 2015 Chinese Intelligent Systems Conference

Print ISBN: 978-3-662-48384-8

Electronic ISBN: 978-3-662-48386-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-662-48386-2_65

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