IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v41y2009i22p2809-2815.html
   My bibliography  Save this article

Forecasting container transshipment in Germany

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840802260932
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036840802260932?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Dohee Kim & Eunju Lee & Imam Mustafa Kamal & Hyerim Bae, 2025. "Long‐term forecasting of maritime economics index using time‐series decomposition and two‐stage attention," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 153-172, January.
    9. 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.
    10. 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.
    11. banerjee, soumya, 2016. "Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data," OSF Preprints ewtcf, Center for Open Science.
    12. Anurag Kulshrestha & Abhishek Yadav & Himanshu Sharma & Shikha Suman, 2024. "A deep learning‐based multivariate decomposition and ensemble framework for container throughput forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2685-2704, November.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:41:y:2009:i:22:p:2809-2815. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.