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Forecasting the Suez Canal traffic: a neural network analysis

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  • Mohamed M. Mostafa

Abstract

Although the Suez Canal is the most important man-made waterway in the world, rivaled perhaps only by the Panama Canal, little research has been done into forecasting its traffic flows. This paper uses both univariate ARIMA (Autoregressive Integrated Moving Average) and Neural network models to forecast the maritime traffic flows in the Suez Canal which are expressed in tons. One of the important strengths of the ARIMA modelling approach is the ability to go beyond the basic univariate model by considering interventions, calendar variations, outliers, or other real aspects of typically observed time series. On the other hand, neural nets have received a great deal of attention over the past few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally been used. The models obtained in this paper provide useful insight into the behaviour of maritime traffic flows since the reopening of the Canal in 1975—following an 8-year closure during the Arab--Israeli wars (1967--1973)—till 1998. The paper also compares the performance of ARIMA models with that of neural networks on an example of a large monthly dataset.

Suggested Citation

  • Mohamed M. Mostafa, 2004. "Forecasting the Suez Canal traffic: a neural network analysis," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(2), pages 139-156, April.
    • Handle: RePEc:taf:marpmg:v:31:y:2004:i:2:p:139-156
    DOI: 10.1080/0308883032000174463
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    Citations

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    Cited by:

    1. Schøyen, Halvor & Bråthen, Svein, 2011. "The Northern Sea Route versus the Suez Canal: cases from bulk shipping," Journal of Transport Geography, Elsevier, vol. 19(4), pages 977-983.
    2. banerjee, soumya, 2016. "Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data," OSF Preprints c3av2, Center for Open Science.
    3. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
    4. Yi Xiao & Shouyang Wang & John J. Liu & Jin Xiao & Yi Hu, 2016. "Throughput estimation based port development and management policies analysis," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(1), pages 84-97, January.
    5. Sangseop Lim & Chang-hee Lee & Won-Ju Lee & Junghwan Choi & Dongho Jung & Younghun Jeon, 2022. "Valuation of the Extension Option in Time Charter Contracts in the LNG Market," Energies, MDPI, vol. 15(18), pages 1-14, September.
    6. Yip, Tsz Leung & Wong, Mei Chi, 2015. "The Nicaragua Canal: scenarios of its future roles," Journal of Transport Geography, Elsevier, vol. 43(C), pages 1-13.
    7. banerjee, soumya, 2016. "Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data," OSF Preprints ewtcf, Center for Open Science.
    8. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    9. Sarat Chandra Nayak & Bijan Bihari Misra, 2019. "A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-34, December.

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