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http://dx.doi.org/10.7469/JKSQM.2019.47.3.497

Prediction of Baltic Dry Index by Applications of Long Short-Term Memory  

HAN, Minsoo (Department of Shipping Management, Postgraduate School of Korea Maritime and Ocean University)
YU, Song-Jin (Department of Shipping Management, Korea Maritime and Ocean University)
Publication Information
Journal of Korean Society for Quality Management / v.47, no.3, 2019 , pp. 497-508 More about this Journal
Abstract
Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.
Keywords
Baltic Dry Index (BDI); Time-series Prediction; Artificial Neural Network (ANN); Recurrent Neural Network (RNN); Long Short-Term Memory (LSTM);
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