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Effective multi-step ahead container throughput forecasting under the complex context

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成果类型:
期刊论文
作者:
Xiao, Yi;Xie, Minghu;Hu, Yi;Yi, Ming
通讯作者:
Yi Xiao
作者机构:
[Xie, Minghu; Yi, Ming; Xiao, Yi] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
[Hu, Yi] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China.
通讯机构:
[Yi Xiao] S
School of Information Management, Central China Normal University, Wuhan, 430079 China
语种:
英文
关键词:
container throughput forecasting;deep learning;empirical wavelet transform;MIMO strategy;temporal convolutional network
期刊:
Journal of Forecasting
ISSN:
0277-6693
年:
2023
卷:
42
期:
7
页码:
1823-1843
基金类别:
Fundamental Research Funds for the Humanities and Social Sciences Layout Foundation of the Ministry of Education of China [21YJA630098]; Fundamental Research Funds for the Central Universities [CCNU22QN017]
机构署名:
本校为第一机构
院系归属:
信息管理学院
摘要:
Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid-19 pandemic. In light of this, this research proposes an effective multi-step ahead forecasting model called EWT-TCN-KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state-of-the-art temporal convolutional network is utilized to predict the decomposed component...

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