A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization

Qi Wu, Chun Hung Roberts Law, Edmond Wu, Jinxing Lin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)

Abstract

In this paper, the relationship between Gaussian noise and the loss function of the support vector regression machine (SVRM) is analyzed, and then a Gaussian loss function proposed to reduce the effect of such noise on the regression estimates. Since the ε-insensitive loss function cannot reduce noise, a novel support vector regression machine, g-SVRM, is proposed, then a chaotic particle swarm optimization (CPSO) algorithm developed to estimate its unknown parameters. Finally, a hybrid-forecasting model combining g-SVRM with the CPSO is proposed to forecast a multi-dimensional time series. The results of two experiments demonstrate the feasibility of this approach.
Original languageEnglish
Pages (from-to)96-110
Number of pages15
JournalInformation Sciences
Volume238
DOIs
Publication statusPublished - 20 Jul 2013

Keywords

  • Chaotic mapping
  • Forecast
  • Gaussian loss function
  • Particle swarm optimization
  • Support vector regression machine

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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