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

  1. Jose Antonio Moscoso-Lopez 1
  2. Juan Jesus Ruiz-Aguilar 1
  3. Javier Gonzalez-Enrique 1
  4. Daniel Urda 1
  5. Hector Mesa 1
  6. Turias, Ignacio J. 1
  1. 1 Universidad de Cádiz

    Universidad de Cádiz

    Cádiz, España


Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Publisher: Springer Suiza

ISBN: 978-3-030-29859-3 978-3-030-29858-6

Year of publication: 2019

Pages: 563-574

Congress: Hybrid Artificial Intelligent Systems (14. 2019. León)

Type: Conference paper


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.