A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting

doi:10.1016/j.apm.2018.01.014

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	A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting
	Niu, Mingfei 1; Hu, Yueyong 1; Sun, Shaolong 2,3,4; Liu, Yu 1
	2018-05-01
发表期刊	APPLIED MATHEMATICAL MODELLING
ISSN	0307-904X
卷号	57 页码:163-178
摘要	This 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.
关键词	Container throughput forecasting Variational mode decomposition Support vector regression Hybridizing grey wolf optimization Hybrid decomposition-ensemble model
DOI	10.1016/j.apm.2018.01.014
语种	英语
资助项目	National Natural Science Foundation of China[71771207] ; National Natural Science Foundation of China[11475073]
WOS研究方向	Engineering ; Mathematics ; Mechanics
WOS类目	Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Mechanics
WOS记录号	WOS:000427219000010
出版者	ELSEVIER SCIENCE INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/29774
专题	中国科学院数学与系统科学研究院
通讯作者	Hu, Yueyong; Sun, Shaolong
作者单位	1.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
推荐引用方式 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.