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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

18 Scopus Citations
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Author(s)

  • Mingfei Niu
  • Yueyong Hu
  • Shaolong Sun
  • Yu Liu

Detail(s)

Original languageEnglish
Pages (from-to)163-178
Journal / PublicationApplied Mathematical Modelling
Volume57
Early online date31 Jan 2018
Publication statusPublished - May 2018

Abstract

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.

Research Area(s)

  • Container throughput forecasting, Hybrid decomposition-ensemble model, Hybridizing grey wolf optimization, Support vector regression, Variational mode decomposition