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Forecasting Bulk Freight Rates with Machine Learning Methods

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(7), pp.127-132
  • DOI : 10.9708/jksci.2021.26.07.127
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : June 28, 2021
  • Accepted : July 26, 2021
  • Published : July 30, 2021

Sangseop Lim 1 Seok-Hun Kim 2

1한국해양대학교
2배재대학교

Accredited

ABSTRACT

This paper applies a machine learning model to forecasting freight rates in dry bulk and tanker markets with wavelet decomposition and empirical mode decomposition because they can refect both information scattered in the time and frequency domain. The decomposition with wavelet is outperformed for the dry bulk market, and EMD is the more proper model in the tanker market. This result provides market players with a practical short-term forecasting method. This study contributes to expanding a variety of predictive methodologies for one of the highly volatile markets. Furthermore, the proposed model is expected to improve the quality of decision-making in spot freight trading, which is the most frequent transaction in the shipping industry.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.