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A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting
Niu, Mingfei1; Hu, Yueyong1; Sun, Shaolong2,3,4; Liu, Yu1
2018-05-01
Source PublicationAPPLIED MATHEMATICAL MODELLING
ISSN0307-904X
Volume57Pages:163-178
AbstractThis paper built a hybrid decomposition-ensemble model named VMD-ARIMA-HGWO-SVR for the purpose of improving the stability and accuracy of container throughput prediction. The latest variational mode decomposition (VMD) algorithm is employed to decompose the original series into several modes (components), then ARIMA models are built to forecast the low-frequency components, and the high-frequency components are predicted by SVR models which are optimized with a recently proposed swarm intelligence algorithm called hybridizing grey wolf optimization (HGWO), following this, the prediction results of all modes are ensembled as the final forecasting result. The error analysis and model comparison results show that the VMD is more effective than other decomposition methods such as CEEMD and WD, moreover, adopting ARIMA models for prediction of low-frequency components can yield better results than predicting all components by SVR models. Based on the results of empirical study, the proposed model has good prediction performance on container throughput data, which can be used in practical work to provide reference for the operation and management of ports to improve the overall efficiency and reduce the operation costs. (C) 2018 Elsevier Inc. All rights reserved.
KeywordContainer throughput forecasting Variational mode decomposition Support vector regression Hybridizing grey wolf optimization Hybrid decomposition-ensemble model
DOI10.1016/j.apm.2018.01.014
Language英语
Funding ProjectNational Natural Science Foundation of China[71771207] ; National Natural Science Foundation of China[11475073]
WOS Research AreaEngineering ; Mathematics ; Mechanics
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Mechanics
WOS IDWOS:000427219000010
PublisherELSEVIER SCIENCE INC
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29774
Collection中国科学院数学与系统科学研究院
Affiliation1.Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Gansu, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
4.City Univ Hong Kong, Dept Syst Engn & Engn Management, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China
Recommended Citation
GB/T 7714
Niu, Mingfei,Hu, Yueyong,Sun, Shaolong,et al. A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting[J]. APPLIED MATHEMATICAL MODELLING,2018,57:163-178.
APA Niu, Mingfei,Hu, Yueyong,Sun, Shaolong,&Liu, Yu.(2018).A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting.APPLIED MATHEMATICAL MODELLING,57,163-178.
MLA Niu, Mingfei,et al."A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting".APPLIED MATHEMATICAL MODELLING 57(2018):163-178.
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