Abstract
This study is focused on achieving a reliable prediction of the daily number of goods subject to inspection at Border Inspections Posts (BIPs). The final aim is to develop a prediction tool in order to aid the decision-making in the inspection process. The best artificial neural network (ANN) model was obtained by applying the Bayesian regularization approach. Furthermore, this study compares daily forecasting with a two-stage forecasting approach using a weekly aggregation-disaggregation procedure. The comparison was made using different performance indices. The BIP of the Port of Algeciras Bay was used as a case study. This approach may become a supporting tool for the prediction of the number of goods subject to inspection at other international inspection facilities.
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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 Competitividad-Spain). The data have been kindly provided by Port Authority of Algeciras Bay.
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Ruiz-Aguilar, J.J., Moscoso-López, J.A., Turias, I., González-Enrique, J. (2018). Forecasting Freight Inspection Volume Using Bayesian Regularization Artificial Neural Networks: An Aggregation-Disaggregation Procedure. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_17
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