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Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models

Industrial Data Science and Engineering, Department of Industrial Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, Korea
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Academic Editor: Rui Araújo
Appl. Sci. 2021, 11(19), 8995; https://doi.org/10.3390/app11198995
Received: 10 August 2021 / Revised: 19 September 2021 / Accepted: 24 September 2021 / Published: 27 September 2021
The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression. However, these methods do not explain the complexity and variability of data caused by changes in the external environment, such as the global financial crisis and economic fluctuations. Deep learning can explore the inherent patterns of data and analyze the characteristics (time series, external environmental variables, and outliers); hence, the accuracy of deep learning-based volume prediction models is better than that of traditional models. However, this does not include the study of overall trends (upward, steady, or downward). In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. The proposed model explores external variables that are related to container volume, combining port volume time-series decomposition with external variables and deep learning-based multivariate long short-term memory (LSTM) prediction. The results indicate that the proposed model performs better than the traditional LSTM model and follows the trend simultaneously. View Full-Text
Keywords: container volume prediction; deep learning; time-series decomposition; external variables; long short-term memory model container volume prediction; deep learning; time-series decomposition; external variables; long short-term memory model
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MDPI and ACS Style

Lee, E.; Kim, D.; Bae, H. Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models. Appl. Sci. 2021, 11, 8995. https://doi.org/10.3390/app11198995

AMA Style

Lee E, Kim D, Bae H. Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models. Applied Sciences. 2021; 11(19):8995. https://doi.org/10.3390/app11198995

Chicago/Turabian Style

Lee, Eunju, Dohee Kim, and Hyerim Bae. 2021. "Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models" Applied Sciences 11, no. 19: 8995. https://doi.org/10.3390/app11198995

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