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http://dx.doi.org/10.5394/KINPR.2020.44.3.187

A Comparative Analysis of the Forecasting Performance of Coal and Iron Ore in Gwangyang Port Using Stepwise Regression and Artificial Neural Network Model  

Cho, Sang-Ho (Graduate School of Korea Maritime Ocean University)
Nam, Hyung-Sik (Shipping Management, Korea Maritime Ocean University)
Ryu, Ki-Jin (Graduate School of Korea Maritime Ocean University)
Ryoo, Dong-Keun (Division of Shipping Management, Korea Maritime Ocean University)
Publication Information
Journal of Navigation and Port Research / v.44, no.3, 2020 , pp. 187-194 More about this Journal
Abstract
It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.
Keywords
Gwangyang port; Iron ore; Coal; Time series analysis; Stepwise regression analysis; Artificial neural network model;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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