Evolutionary optimization of multi-parametric kernel ε-SVMr for forecasting problems

J. Gascón-Moreno*, E. G. Ortiz-García, S. Salcedo-Sanz, L. Carro-Calvo, B. Saavedra-Moreno, A. Portilla-Figueras

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In this paper, we propose a novel multi-parametric kernel Support Vector Regression algorithm (SVMr) optimized with an evolutionary technique, specially well suited for forecasting problems. The multi-parametric SVMr model and the evolutionary algorithm proposed are both described in detail in the paper. In addition, several new bounds for the multi-parametric kernel considered are obtained, in such a way that the SVMr hyper-parameters' search space is reduced. We present experimental evidences of the good performance of the evolutionary algorithm for optimizing the multi-parametric kernel, when compared to a standard SVMr with a Grid Search approach. Specifically, results in different real regression problems from public repositories are obtained, and also a real application focused on the short-term temperature prediction at Barcelona's airport. The results obtained have shown the good performance of the multi-parametric kernel approach both in accuracy and computation time.

Original languageEnglish (US)
Pages (from-to)213-221
Number of pages9
JournalSoft Computing
Issue number2
StatePublished - Feb 2013

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology


Dive into the research topics of 'Evolutionary optimization of multi-parametric kernel ε-SVMr for forecasting problems'. Together they form a unique fingerprint.

Cite this