Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM

Cheng-Hong Yang and Po-Yin Chang
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Cheng-Hong Yang: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Po-Yin Chang: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan

Mathematics, 2020, vol. 8, issue 10, 1-17

Abstract: Forecasting the demand for container throughput is a critical indicator to measure the development level of a port in global business management and industrial development. Time-series analysis approaches are crucial techniques for forecasting the demand for container throughput. However, accurate demand forecasting for container throughput remains a challenge in time-series analysis approaches. In this study, we proposed a mixed-precision neural architecture to forecasting the demand for container throughput. This study is the first work to use a mixed-precision neural network to forecast the container throughput—the mixed-precision architecture used the convolutional neural network for learning the strength of the features and used long short-term memory to identify the crucial internal representation of time series depending on the strength of the features. The experiments on the demand for container throughput of the five ports in Taiwan were conducted to compare our deep learning architecture with other forecasting approaches. The results indicated that our mixed-precision neural architecture exhibited higher forecasting performance than classic machine learning approaches, including adaptive boosting, random forest regression, and support vector regression. The proposed architecture can effectively predict the demand for port container throughput and effectively reduce the costs of planning and development of ports in the future.

Keywords: container throughput forecasting; convolutional neural network (CNN); deep learning; long short-term memory (LSTM); recurrent neural network (RNN) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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