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A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput

  • Published:2019-11 Issue: Volume:534 Page:122025
  • ISSN:0378-4371
  • Container-title:Physica A: Statistical Mechanics and its Applications
  • language:en
  • Short-container-title:Physica A: Statistical Mechanics and its Applications

Author:

Li Hongtao,Bai Juncheng,Li Yongwu

Funder

National Natural Science Foundation of China

Lanzhou Jiaotong University–Tianjin University Innovation Fund

Beijing Natural Science Foundation

Great Wall Scholar Training Program of Beijing Municipality

Beijing University of Technology

Publisher

Elsevier BV

Subject

Condensed Matter Physics,Statistics and Probability

Reference56 articles.

1. GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries;Mo;Appl. Soft Comput. J.,2018

2. A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports;Rashed;Transp. Res. A,2018

3. A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting;Niu;Appl. Math. Model.,2018

4. The vulnerability of the global container shipping network to targeted link disruption;Viljoen;Physica A,2016

5. Forecasting container throughputs with domain knowledge;Huang;Procedia Comput. Sci.,2014

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