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Learning Bayesian Network to Predict Group Emotion in Kindergarten by Evolutionary Computation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Forecasting Freight Inspection Volume Using Bayesian Regularization Artificial Neural Networks: An Aggregation-Disaggregation Procedure

Authors : Juan Jesús Ruiz-Aguilar, José Antonio Moscoso-López, Ignacio Turias, Javier González-Enrique

Published in: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Publisher: Springer International Publishing

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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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Metadata
Title
Forecasting Freight Inspection Volume Using Bayesian Regularization Artificial Neural Networks: An Aggregation-Disaggregation Procedure
Authors
Juan Jesús Ruiz-Aguilar
José Antonio Moscoso-López
Ignacio Turias
Javier González-Enrique
Copyright Year
2018
Book
International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding

Print ISBN: 978-3-319-67179-6

Electronic ISBN: 978-3-319-67180-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-67180-2_17

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