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Forecasting container transshipment in Germany

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  • Peter Schulze
  • Alexander Prinz

Abstract

In this article, we examine container transshipment at German ports using the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Holt-Winters exponential smoothing approach. Our models are designed especially to take account of the seasonal behaviour of the quarterly data used. We consider the dynamic development in this sector for the whole container throughput and also the destinations Asia, Europe and North America, which are the world's three main economic regions. Our data runs from the first quarter of 1989 to the fourth quarter of 2006. We provide detailed quarterly forecasts for the years 2007 and 2008. According to forecasting error measures such as mean square error and Theil's U, the SARIMA-approach yields slightly better values of modelling the container throughput than the exponential smoothing approach. Our forecast results indicate further strong growth for German container handling in total and especially for the destinations Asia and Europe. Only the container transshipment between Germany and North America shows rather small increases up to the end of 2008.

Suggested Citation

  • Peter Schulze & Alexander Prinz, 2009. "Forecasting container transshipment in Germany," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2809-2815.
    • Handle: RePEc:taf:applec:v:41:y:2009:i:22:p:2809-2815
    DOI: 10.1080/00036840802260932
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    Citations

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    Cited by:

    1. Akhter, Tahsina, 2013. "Short-Term Forecasting of Inflation in Bangladesh with Seasonal ARIMA Processes," MPRA Paper 43729, University Library of Munich, Germany.
    2. Marco Ferretti & Ugo Fiore & Francesca Perla & Marcello Risitano & Salvatore Scognamiglio, 2022. "Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development," Future Internet, MDPI, vol. 14(8), pages 1-19, July.
    3. Cheng-Hong Yang & Po-Yin Chang, 2020. "Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
    4. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.
    5. banerjee, soumya, 2016. "Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data," OSF Preprints c3av2, Center for Open Science.
    6. Yi Xiao & Shouyang Wang & Ming Xiao & Jin Xiao & Yi Hu, 2017. "The Analysis for the Cargo Volume with Hybrid Discrete Wavelet Modeling," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 851-863, May.
    7. Andre Jungmittag, 2016. "Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 373-380, July.
    8. Truong Ngoc Cuong & Le Ngoc Bao Long & Hwan-Seong Kim & Sam-Sang You, 2023. "Data analytics and throughput forecasting in port management systems against disruptions: a case study of Busan Port," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 61-89, March.
    9. Gu Pang & Bartosz Gebka, 2017. "Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2454-2469, May.
    10. banerjee, soumya, 2016. "Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data," OSF Preprints ewtcf, Center for Open Science.
    11. Nyoni, Thabani, 2019. "Sri Lanka – the wonder of Asia: analyzing monthly tourist arrivals in the post-war era," MPRA Paper 96790, University Library of Munich, Germany.
    12. Su-Han Woo & Stephen Pettit & Anthony Beresford & Dong-Wook Kwak, 2012. "Seaport Research: A Decadal Analysis of Trends and Themes Since the 1980s," Transport Reviews, Taylor & Francis Journals, vol. 32(3), pages 351-377, January.
    13. M. Milenković & N. Milosavljevic & N. Bojović & S. Val, 2021. "Container flow forecasting through neural networks based on metaheuristics," Operational Research, Springer, vol. 21(2), pages 965-997, June.
    14. Truong Ngoc Cuong & Sam-Sang You & Le Ngoc Bao Long & Hwan-Seong Kim, 2022. "Seaport Resilience Analysis and Throughput Forecast Using a Deep Learning Approach: A Case Study of Busan Port," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    15. 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.

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