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http://dx.doi.org/10.9708/jksci.2021.26.07.127

Forecasting Bulk Freight Rates with Machine Learning Methods  

Lim, Sangseop (Div. of Navigation Convergence Studies, Korea Maritime and Ocean University)
Kim, Seokhun (Dept. of Electronic Commerce, PaiChai University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.7, 2021 , pp. 127-132 More about this Journal
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.
Keywords
Baltic Freight Rate; BCI; BPI; BDTI; BCTI; Decomposition; ANN;
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