Predicting time series with support vector machines

Klaus Muller, A. J. Smoła, G. Rätsch, B. Schölkopf, J. Kohlmorgen, V. Vapnik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

563 Citations (Scopus)

Abstract

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an ε insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise) Mackey Glass equation and (b) the Santa Fe competition (set D). In both cases Support Vector Machines show an excellent performance. In case (b) the Support Vector approach improves the best known result on the benchmark by a factor of 29%.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages999-1004
Number of pages6
Volume1327
ISBN (Print)3540636315, 9783540636311
Publication statusPublished - 1997
Externally publishedYes
Event7th International Conference on Artificial Neural Networks, ICANN 1997 - Lausanne, Switzerland
Duration: 1997 Oct 81997 Oct 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1327
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Conference on Artificial Neural Networks, ICANN 1997
CountrySwitzerland
CityLausanne
Period97/10/897/10/10

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Muller, K., Smoła, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1327, pp. 999-1004). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1327). Springer Verlag.