Forecasting Port Container Throughput with Deep Learning Approach

Fuxin Jiang, Gang Xie, Shouyang Wang
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引用次数: 1

Abstract

Due to the international transfer of manufacturing industry, the change of trade policy and frequent irregular events in the global trade, it becomes more difficult to predict port container throughput accurately. In order to improve the predictive accuracy, we develop a bidirectional long short-term memory network model to forecast the throughput. Using the data of port in Qingdao, this study investigates for the first time how to use the deep learning approach to predict port container throughput. The empirical results show that the proposed model can achieve highest average predictive accuracy, which indicates that the approach is effective in the increasingly complex trade situation.
基于深度学习方法的港口集装箱吞吐量预测
由于制造业的国际转移、贸易政策的变化以及全球贸易中不规则事件的频繁发生,对港口集装箱吞吐量的准确预测变得更加困难。为了提高预测精度,我们建立了双向长短期记忆网络模型来预测吞吐量。本文以青岛港为例,首次探讨了如何利用深度学习方法对港口集装箱吞吐量进行预测。实证结果表明,该模型能够达到最高的平均预测精度,表明该方法在日益复杂的贸易形势下是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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