.png){kind=icon}
返回
Forecasting container freight rates using the Prophet forecasting method
.png){kind=icon}
OA
AI
DOI：10.1016/j.tranpol.2023.01.012.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This study applies three innovative methods in forecasting container freight rates. Firstly, we extracted 471 major disruptive events from the 'Lloyds List' database from 2010 until 2020, that may affect freight rates. Secondly, we use Machine Learning (ML) and natural language processing techniques to categorize these events into six distinct categories. These include: congestion, peak demand, policy, price up, overcapacity, and coronavirus. Thirdly, we apply Prophet forecasting on six major container routes by incorporating the six categories of events. The results reveal that 'overcapacity' and 'coronavirus' led to improved forecasting accuracy of freight rates when compared to the Prophet model without accounting for events. This study provides a more reliable mixed-method approach to improving the accuracy of container freight rate forecasts. The proposed forecasting technique will help policy makers and practitioners to develop and deploy strategies to mitigate the risks associated with the volatility of freight rates and supply chain costings.
Keyword:
Prophet forecasting
COVID-19
Machine learning
Natural language processing
Container freight
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.3
论文数:
3.3K
被引数:
1.4W
机构
引用论文
Precision measurement and nondestructive testing by means of digital phase shifting speckle pattern and speckle pattern shearing interferometry.png)
Measurement
MeasurementIF0
PREAI
分享收藏分享收藏Container freight rate forecasting with improved accuracy by integrating soft facts from practitioners通过整合从业人员的软事实提高集装箱运价预测的准确性OAAI
OAAI分享收藏
查看更多内容