A Grey hybrid model with industry share for the forecasting of cargo volumes and dynamic industrial changes

The importance of ocean shipping for international trade forecasting is growing due to the specialization and evolution of industrial sectors around the world. Classic approaches for cargo volume forecasting, such as time series and casual methods, may have poor performances if the data size is small with large fluctuations. This study proposes a hybrid forecasting model based on the Grey forecasting models and an industry share transformation technique. The hybrid model is particular useful for problems when there are dynamic changes in the industry share and the sample size in historical dataset is small. Using a case study of cargo export and import by industry between Taiwan and North American, the proposed model shows good forecasting performances. The findings can be useful for the marine carriers in responding to the dynamic industrial changes.

• Record URL:
  https://doi.org/10.1080/19427867.2018.1502505
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19427867
• Supplemental Notes:
  • © 2018 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Hsu, Chaug-Ing
  • Huang, Yu-Che
  • Wong, Ka Io
• Publication Date: 2020-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 25-36
• Serial:
  • Transportation Letters: The International Journal of Transportation Research
  • Volume: 12
  • Issue Number: 1
  • Publisher: Taylor & Francis
  • ISSN: 1942-7867
  • EISSN: 1942-7875
  • Serial URL: http://www.tandfonline.com/toc/ytrl20/current

Subject/Index Terms

• TRT Terms: Case studies; Exports; Forecasting; Freight traffic; Imports; International trade; Ocean shipping; Traffic volume
• Geographic Terms: North America; Taiwan
• Subject Areas: Freight Transportation; Marine Transportation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01728064
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 28 2020 9:42AM