IDEAS home Printed from https://ideas.repec.org/a/taf/marpmg/v48y2021i8p1096-1108.html
   My bibliography  Save this article

Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models

Author

Listed:
  • Kaan Koyuncu
  • Leyla Tavacioğlu
  • Neslihan Gökmen
  • Umut Çelen Arican

Abstract

Maritime operators are facing their biggest challenge called Coronavirus (COVID-19) since the 2008 financial crisis. As part of the measures taken by the countries against the virus, the domino effect started with the breaks in the interconnected supply chain, like the spider web. The main purpose of this study is modeling the Institute of Shipping Economics and Logistics (ISL) and the Leibniz-Institut für Wirtschaftsforschung (RWI) Container Throughput Index with the time series, to reveal the relationship between the short-term forecast results and the COVID-19 seen in the first months of 2020. The deep effect of COVID-19 on maritime trade is investigated by forecasting the RWI/ISL Container Throughput Index in 89 major international container ports, including and excluding seasonal variations. The modeling process of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing State Space Model (ETS) is explained. To evaluate SARIMA and ETS models’ performance, information criteria, and error measurements are calculated and compared. SARIMA model is found as more suitable model than ETS forecasting seasonally and working-day adjusted and original RWI/ISL. The results indicated that the SARIMA model is suitable and efficient for the forecasting of RWI/ISL. Three months’ forecasting results are showed that the decrease will continue.

Suggested Citation

  • Kaan Koyuncu & Leyla Tavacioğlu & Neslihan Gökmen & Umut Çelen Arican, 2021. "Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models," Maritime Policy & Management, Taylor & Francis Journals, vol. 48(8), pages 1096-1108, November.
    • Handle: RePEc:taf:marpmg:v:48:y:2021:i:8:p:1096-1108
    DOI: 10.1080/03088839.2021.1876937
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03088839.2021.1876937
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03088839.2021.1876937?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. Yang, Yang & Liu, Qing & Chang, Chia-Hsun, 2023. "China-Europe freight transportation under the first wave of COVID-19 pandemic and government restriction measures," Research in Transportation Economics, Elsevier, vol. 97(C).
    2. 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.
    3. Lu, Bo & Xu, Xin, 2024. "Digital transformation and port operations: Optimal investment under incomplete information," Transport Policy, Elsevier, vol. 151(C), pages 134-146.
    4. Zhao, Chuan & Li, Xin & Zuo, Min & Mo, Lipo & Yang, Changchun, 2022. "Spatiotemporal dynamic network for regional maritime vessel flow prediction amid COVID-19," Transport Policy, Elsevier, vol. 129(C), pages 78-89.
    5. Ahmed Karam & Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & El-Awady Attia, 2022. "A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions," IJERPH, MDPI, vol. 19(19), pages 1-27, September.
    6. 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.
    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. Tvedt, Jostein & Hovi, Inger Beate, 2024. "Container shipping: A market equilibrium perspective on freight rates formation post-Covid-19," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

    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:marpmg:v:48:y:2021:i:8:p:1096-1108. 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/TMPM20 .

    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.