Forecasting Shanghai Container Freight Index
Playlist
  • Shanghai Containerized Freight Index
  • long short-term memory
  • seasonal autoregressive integrated moving average
  • deep learning
  • machine learning
  • forecasting
Chapter
00:12
Research Background
01:20
Research Question
01:23
Models, Data and Results
02:50
Conclusions and Contributions
Video Introduction

This video is adapted from 10.3390/jmse10050593

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. 

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Hirata, E.; Matsuda, T. Forecasting Shanghai Container Freight Index. Encyclopedia. Available online: https://encyclopedia.pub/video/video_detail/1432 (accessed on 23 December 2024).
Hirata E, Matsuda T. Forecasting Shanghai Container Freight Index. Encyclopedia. Available at: https://encyclopedia.pub/video/video_detail/1432. Accessed December 23, 2024.
Hirata, Enna, Takuma Matsuda. "Forecasting Shanghai Container Freight Index" Encyclopedia, https://encyclopedia.pub/video/video_detail/1432 (accessed December 23, 2024).
Hirata, E., & Matsuda, T. (2024, December 05). Forecasting Shanghai Container Freight Index. In Encyclopedia. https://encyclopedia.pub/video/video_detail/1432
Hirata, Enna and Takuma Matsuda. "Forecasting Shanghai Container Freight Index." Encyclopedia. Web. 05 December, 2024.
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