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

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 (BDI time series) at point of time  by 8 variables (related to the dry bulk market) of  at point of time  . 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. ● Received 5 May 2019, 1st revised 16 June, accepted 17 June 2019 † Corresponding Author(coppers@kmou.ac.kr) c 2019, The Korean Society for Quality Management This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-Commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ISSN 1229-1889(Print) ISSN 2287-9005(Online) J Korean Soc Qual Manag Vol. 47, No.3: 497-508, September 2019 https://dx.doi.org/10.7469/JKSQM.2019.47.3.497 498 J Korean Soc Qual Manag Vol. 47, No. 3: 497-508, September 2019