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Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

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  • Ruiz-Aguilar, J.J.
  • Turias, I.J.
  • Jiménez-Come, M.J.

Abstract

In this paper, the number of goods subject to inspection at European Border Inspections Post are predicted using a hybrid two-step procedure. A hybridization methodology based on integrating the data obtained from autoregressive integrated moving averages (SARIMA) model in the artificial neural network model (ANN) to predict the number of inspections is proposed. Several hybrid approaches are compared and the results indicate that the hybrid models outperform either of the models used separately. This methodology may become a powerful decision-making tool at other inspection facilities of international seaports or airports.

Suggested Citation

  • Ruiz-Aguilar, J.J. & Turias, I.J. & Jiménez-Come, M.J., 2014. "Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 1-13.
    • Handle: RePEc:eee:transe:v:67:y:2014:i:c:p:1-13
    DOI: 10.1016/j.tre.2014.03.009
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    Cited by:

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    9. Huang, Dong & Grifoll, Manel & Sanchez-Espigares, Jose A. & Zheng, Pengjun & Feng, Hongxiang, 2022. "Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic," Transport Policy, Elsevier, vol. 128(C), pages 1-12.
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