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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Acknowledgments
This work is part of the ACERINOX EUROPA S.A.U research project AUSINOX IDI-20170081 - “Obtaining austenitic stainless steels with minimum inclusion content from the development of new advanced simulation models in melting shop processes”, supported by CDTI (Centro para el Desarrollo Tecnológico Industrial), Spain. This project has been co-financed by the European Regional Development Fund (FEDER), within the Intelligent Growth Operational Program 2014–2020, with the aim of promoting research, technological development and innovation. Authors acknowledge support through grant RTI2018-098160-B-I00 from MINECO-SPAIN which include FEDER funds. The database has been kindly provided by the Port Authority of Algeciras Bay.
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Moscoso-Lopez, J.A., Ruiz-Aguilar, J.J., Gonzalez-Enrique, J., Urda, D., Mesa, H., Turias, I.J. (2019). Ro-Ro Freight Prediction Using a Hybrid Approach Based on Empirical Mode Decomposition, Permutation Entropy and Artificial Neural Networks. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_48
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