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The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications

SOCO’18-CISIS’18-ICEUTE’18 2018: International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 pp 357–366Cite as

SVR-Ensemble Forecasting Approach for Ro-Ro Freight at Port of Algeciras (Spain)

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 771))

Abstract

The forecasting of the freight transportation provides a helpful information in the management of ports environment and can be used as a decision-making tool. This work addresses the forecasting of ro-ro (roll-on roll-off) freight flow in a port using a two-stage approach by an ensemble of the best Support Vector Regression (SVR) models. The time series used for forecasting is daily ro-ro freight in the port of Algeciras during the period from 2000 to 2007. Additionally, the time series was preprocessed through an exponential smoothing in order to improve the performance. The experiment results show that the proposed approach is a promising tool in freight forecasting.

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Acknowledgments

This work is part of the coordinated research projects TIN2014-58516-C2-1-R and TIN2014-58516-C2-2-R supported by (MICINN Ministerio de Economía y Competi-tividad-Spain). The data have been kindly provided by Port Authority of Algeciras Bay.

Author information

Authors and Affiliations

  1. Intelligent Modelling of Systems Research Group, Polytechnic School of Engineering (Algeciras), University of Cádiz, Avda. Ramón Puyol s/n, 11202, Algeciras, Cádiz, Spain

    Jose Antonio Moscoso-López, Ignacio J. Turias, Juan Jesús Ruiz Aguilar & Francisco Javier Gonzalez-Enrique

Authors
  1. Jose Antonio Moscoso-López
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  2. Ignacio J. Turias
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  3. Juan Jesús Ruiz Aguilar
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  4. Francisco Javier Gonzalez-Enrique
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Corresponding author

Correspondence to Jose Antonio Moscoso-López .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    Manuel Graña

  2. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country, Sarriena, Vizcaya, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Salamanca, Spain

    José Antonio Sáez

  6. Department of Industrial Engineering, University of A Coruña, La Coruña, La Coruña, Spain

    Héctor Quintián

  7. University of Salamanca, Salamanca, Salamanca, Spain

    Emilio Corchado

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Moscoso-López, J.A., Turias, I.J., Aguilar, J.J.R., Gonzalez-Enrique, F.J. (2019). SVR-Ensemble Forecasting Approach for Ro-Ro Freight at Port of Algeciras (Spain). In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_34

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