Shipping market forecasting by forecast combination mechanism

Ruobin Gao, Jiahui Liu, Liang Du, Kum Fai Yuen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The volatile characteristics of the tanker market pose challenges to forecasting. In addition, the volatile characteristics of newbuilding and secondhand ship prices, time charter rates, and scrap values make developing a unified framework of forecasting difficult. Most researchers have developed forecasting models and evaluated their performance based on a specific market. Such narrow development imposes difficulty for practitioners to choose a suitable model. Due to the boom of machine learning, many researchers are trying to boost the forecasting accuracy of shipping markets using machine learning. However, there are many hyper-parameters of the complex machine learning models and a slight variation of the model may cause significant performance degradation. This paper utilizes a forecast combination mechanism to forecast many time series collected from the shipping market, including newbuilding and secondhand ship prices, scrap values, and time charter rates. The models inside the combination pool are just linear functions. Finally, we compare their performance with conventional machine learning models and naïve forecasts using three error metrics and statistical tests. The statistical tests show that the combination of linear models is superior. The findings of this study also indicate that complex models do not boost forecasting accuracy necessarily.

Original languageEnglish
Pages (from-to)1059-1074
Number of pages16
JournalMaritime Policy and Management
Volume49
Issue number8
DOIs
Publication statusPublished - 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.

ASJC Scopus Subject Areas

  • Geography, Planning and Development
  • Transportation
  • Ocean Engineering
  • Management, Monitoring, Policy and Law

Keywords

  • Forecast combination
  • forecasting
  • machine learning
  • shipping market

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