Abstract
The market for tramp shipping is an industry characterized by volatility that is determined by the principle of perfect competition according to supply and demand. The factors affecting the supply and demand of the market are very diverse, and the mechanism of action is complicated, so forecasting in the shipping market remains a difficult task. To solve this problem, time series analysis has been conducted for a long time. However, there have been some limitations in the time series analysis which did not reflect various indicators. For this reason, most of the previous research has been stayed at an initial stage of research by comparing the prediction accuracy of two approaches : the time series analysis and the deep learning method. There has not been much research on how to improve the model performance. The purpose of this study is to improve the Baltic Dry Index(BDI) prediction performance by using Long Short Term Memory(LSTM) which is one of the deep neural networks and to consider additional variables that influence on the previous research.
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