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

文献类型：期刊论文

作者Niu, Mingfei1; Hu, Yueyong1; Sun, Shaolong2,3,4; Liu, Yu1
刊名APPLIED MATHEMATICAL MODELLING
出版日期2018-05-01
卷号57页码:163-178
关键词Container throughput forecasting Variational mode decomposition Support vector regression Hybridizing grey wolf optimization Hybrid decomposition-ensemble model
ISSN号0307-904X
DOI10.1016/j.apm.2018.01.014
英文摘要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.
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
语种英语
WOS记录号WOS:000427219000010
出版者ELSEVIER SCIENCE INC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/29773]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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