The forecasting model based on modified SVRM and PSO penalizing Gaussian noise

Qi Wu, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

The -insensitive loss function has no penalizing capability for white (Gaussian) noise from training series in support vector regression machine (SVRM). To overcome the disadvantage, the relation between Gaussian noise model and loss function of SVRM is studied. And then, a new loss function is proposed to penalize the Gaussian noise in this paper. Based on the proposed loss function, a new ν-SVRM, which is called g-SVRM, is put forward to deal with training set. To seek the optimal parameters of g-SVRM, an improved particle swarm optimization is also proposed. The results of application in car sale forecasts show that the forecasting approach based on the g-SVRM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than ν-SVRM and other traditional methods.
Original languageEnglish
Pages (from-to)1887-1894
Number of pages8
JournalExpert Systems with Applications
Volume38
Issue number3
DOIs
Publication statusPublished - 1 Mar 2011

Keywords

  • Adaptive mutation
  • Forecasting
  • Gaussian loss function
  • Particle swarm optimization
  • Support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

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