2022年4月27日
Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment
Journal of Marine Science and Engineering
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プレプリント・著者最終稿
回数 : 293
- ,
- 巻
- 10
- 号
- 5
- 開始ページ
- 593
- 終了ページ
- 593
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.3390/jmse10050593
- 出版者・発行元
- {MDPI} {AG}
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.
- リンク情報
- ID情報
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- DOI : 10.3390/jmse10050593
- eISSN : 2077-1312
- ORCIDのPut Code : 112351923