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Testing Modified Confusion Entropy as Split Criterion for Decision Trees Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Ro-Ro Freight Prediction Using a Hybrid Approach Based on Empirical Mode Decomposition, Permutation Entropy and Artificial Neural Networks

Authors : Jose Antonio Moscoso-Lopez, Juan Jesus Ruiz-Aguilar, Javier Gonzalez-Enrique, Daniel Urda, Hector Mesa, Ignacio J. Turias

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

This study attempts to create an optimal forecasting model of daily Ro-Ro freight traffic at ports by using Empirical Mode Decomposition (EMD) and Permutation Entropy (PE) together with an Artificial Neural Networks (ANNs) as a learner method.
EMD method decomposes the time series into several simpler subseries easier to predict. However, the number of subseries may be high. Thus, the PE method allows identifying the complexity degree of the decomposed components in order to aggregate the least complex, significantly reducing the computational cost. Finally, an ANNs model is applied to forecast the resulting subseries and then an ensemble of the predicted results provides the final prediction.
The proposed hybrid EMD-PE-ANN method is more robust than the individual ANN model and can generate a high-accuracy prediction. This methodology may be useful as an input of a Decision Support System (DSS) at ports as well it provides relevant information to plan in advance in the port community.

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Metadata
Title
Ro-Ro Freight Prediction Using a Hybrid Approach Based on Empirical Mode Decomposition, Permutation Entropy and Artificial Neural Networks
Authors
Jose Antonio Moscoso-Lopez
Juan Jesus Ruiz-Aguilar
Javier Gonzalez-Enrique
Daniel Urda
Hector Mesa
Ignacio J. Turias
Copyright Year
2019
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-030-29858-6

Electronic ISBN: 978-3-030-29859-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-29859-3_48

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