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Short-term forecast of container throughout: An ARIMA-intervention model for the port of Antwerp

Author

Listed:
  • Yasmine Rashed

    (Faculty Applied Economics, Department of Transport and Regional Economics City Campus (CST) B.432)

  • Hilde Meersman

    (Faculty Applied Economics, Department of Transport and Regional Economics City Campus (CST) B.432)

  • Eddy Van de Voorde

    (Faculty Applied Economics, Department of Transport and Regional Economics City Campus (CST) B.432)

  • Thierry Vanelslander

    (Faculty Applied Economics, Department of Transport and Regional Economics City Campus (CST) B.432)

Abstract

Short-term forecasts of container throughput are essential for planning both port operations and hinterland activities. However, the volatility and uncertainty in global economic activity and, consequently, in seaborne trade introduce complexity in modelling and forecasting container throughput at the port level. In this article, different univariate time series approaches were applied; the autoregressive integrated moving average (ARIMA) model, namely the ARIMA-intervention model, and the ARIMAX model with leading economic indicator. The advantage of the methodology applied is two-fold; (i) it provides insight about the data generating process post-2008 financial crisis and (ii) it identifies the relationship between economic activity and container throughput. Monthly data for the total container throughput at the port of Antwerp was used for the period January 1995–March 2015. On the basis of the empirical analysis and the assessment of the forecasting performance, the EU industrial confidence indicator turned out to lead the container throughput for 2 months. In addition, the incorporation of the structural break of October 2008 showed that, given the conditions, container throughput was persistent to return to the pre-crisis level.

Suggested Citation

  • Yasmine Rashed & Hilde Meersman & Eddy Van de Voorde & Thierry Vanelslander, 2017. "Short-term forecast of container throughout: An ARIMA-intervention model for the port of Antwerp," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 749-764, December.
  • Handle: RePEc:pal:marecl:v:19:y:2017:i:4:d:10.1057_mel.2016.8
    DOI: 10.1057/mel.2016.8
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    References listed on IDEAS

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    Cited by:

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    2. Zhu, Siying & Jia, Shuai & Sun, Qinghe & Meng, Qiang, 2023. "An empirical study of China–Singapore International Land–Sea Trade Corridor: Analysis from supply and demand sides," Transport Policy, Elsevier, vol. 135(C), pages 1-10.
    3. Francesco Parola & Giovanni Satta & Theo Notteboom & Luca Persico, 2021. "Revisiting traffic forecasting by port authorities in the context of port planning and development," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(3), pages 444-494, September.
    4. Zhao, Hong-Mei & He, Hong-Di & Lu, Kai-Fa & Han, Xiao-Long & Ding, Yi & Peng, Zhong-Ren, 2022. "Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19," Transport Policy, Elsevier, vol. 118(C), pages 91-100.
    5. Rashed, Yasmine & Meersman, Hilde & Sys, Christa & Van de Voorde, Eddy & Vanelslander, Thierry, 2018. "A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 127-141.
    6. Li, Chuchu & Lin, Qin & Huang, Dong & Grifoll, Manel & Yang, Dong & Feng, Hongxiang, 2023. "Is entropy an indicator of port traffic predictability? The evidence from Chinese ports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    7. Anqiang Huang & Xinjun Liu & Changrui Rao & Yi Zhang & Yifan He, 2022. "A New Container Throughput Forecasting Paradigm under COVID-19," Sustainability, MDPI, vol. 14(5), pages 1-20, March.
    8. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.

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