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http://dx.doi.org/10.38121/kpea.2020.06.36.2.53

Forecasting the Container Volumes of Busan Port using LSTM  

Kim, Doo-hwan (동아대학교 스마트물류연구센터)
Lee, Kangbae (동아대학교 경영정보학과)
Publication Information
Journal of Korea Port Economic Association / v.36, no.2, 2020 , pp. 53-62 More about this Journal
Abstract
The maritime and port logistics industry is closely related to global trade and economic activity, especially for Korea, which is highly dependent on trade. As the largest port in Korea, Busan Port processes 75% of the country's container cargo; the port is therefore extremely important in terms of the country's national competitiveness. Port container cargo volume forecasts influence port development and operation strategies, and therefore require a high level of accuracy. However, due to unexpected and sudden changes in the port and maritime transportation industry, it is difficult to increase the accuracy of container volume forecasting using existing time series models. Among deep learning models, this study uses the LSTM model to enhance the accuracy of container cargo volume forecasting for Busan Port. To evaluate the model's performance, the forecasting accuracies of the SARIMA and LSTM models are compared. The findings reveal that the forecasting accuracy of the LSTM model is higher than that of the SARIMA model, confirming that the forecasted figures fully reflect the actual measurement figures.
Keywords
container volumes forecasting; deep learning; LSTM;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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