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http://dx.doi.org/10.9708/jksci.2021.26.10.179

A Study on the Forecasting of Bunker Price Using Recurrent Neural Network  

Kim, Kyung-Hwan (Korea Institute of Maritime and Fisheries Technology)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.10, 2021 , pp. 179-184 More about this Journal
Abstract
In this paper, we propose the deep learning-based neural network model to predict bunker price. In the shipping industry, since fuel oil accounts for the largest portion of ship operation costs and its price is highly volatile, so companies can secure market competitiveness by making fuel oil purchasing decisions based on rational and scientific method. In this paper, short-term predictive analysis of HSFO 380CST in Singapore is conducted by using three recurrent neural network models like RNN, LSTM, and GRU. As a result, first, the forecasting performance of RNN models is better than LSTM and GRUs using long-term memory, and thus the predictive contribution of long-term information is low. Second, since the predictive performance of recurrent neural network models is superior to the previous studies using econometric models, it is confirmed that the recurrent neural network models should consider nonlinear properties of bunker price. The result of this paper will be helpful to improve the decision quality of bunker purchasing.
Keywords
Bunker price forecasting; RNN; LSTM; GRU;
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