Resumen de Ro-Ro Freight Prediction Using a Hybrid Approach Based on Empirical Mode Decomposition, Permutation Entropy and Artificial Neural Networks
José Antonio Moscoso López, Juan Jesús Ruiz Aguilar 
, Francisco Javier González Enrique, Daniel Urda Muñoz , Héctor Mesa Jiménez, Ignacio José Turias Domínguez
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.
