Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment
Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment
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DOI：
10.3390/jmse10050593
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发表时间：
2022-04
影响因子：
2.9
通讯作者：
Enna Hirata;Takuma Matsuda
中科院分区：
文献类型：
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作者：
Enna Hirata;Takuma Matsuda
随着大型数据集的可用性越来越高以及预测算法的改进，基于机器学习的技术，特别是深度学习算法，正变得越来越流行。然而，深度学习算法尚未广泛应用于预测集装箱运价。本文比较了长短期记忆（LSTM）方法和季节性自回归积分移动平均（SARIMA）方法对基于航线的综合上海集装箱运价指数（SCFI）的预测效果。研究结果表明，LSTM深度学习模型在大多数数据集中的表现优于SARIMA模型。对于南美和美国东海岸的航线，与SARIMA相比，LSTM可以将预测误差减少85%。SARIMA模型在预测日本西部和东部航线的货运量方面表现优于LSTM。这项研究从四个方面对文献作出了贡献。首先，它提供了提高预测准确性的见解。第二，它有助于有关各方了解集装箱货运市场的趋势，以便作出更明智的决策。第三，它有助于相关利益相关者了解集装箱航运市场的整体趋势。最后，它可以帮助对冲运费的波动。
With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been widely applied to predict container freight rates. In this paper, we compare a long short-term memory (LSTM) method and a seasonal autoregressive integrated moving average (SARIMA) method for forecasting the comprehensive and route-based Shanghai Containerized Freight Index (SCFI). The research findings indicate that the LSTM deep learning models outperformed SARIMA models in most of the datasets. For South America and the east coast of the U.S. routes, LSTM could reduce forecasting errors by as much as 85% compared to SARIMA. The SARIMA models performed better than LSTM in predicting freight movements on the west and east Japan routes. The study contributes to the literature in four ways. First, it presents insights for improving forecasting accuracy. Second, it helps relevant parties understand the trends of container freight markets for wiser decision-making. Third, it helps relevant stakeholders understand overall container shipping market trends. Lastly, it can help hedge against the volatility of freight rates.