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Predicting time series with support vector machines

  • Part VII: Prediction, Forecasting and Monitoring
  • Conference paper
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Book cover Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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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 e 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%.

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Author information

Authors and Affiliations

  1. GMD FIRST, Rudower Chaussee 5, 12489, Berlin, Germany

    K. -R. Müller, A. J. Smola, G. Rätsch & J. Kohlmorgen

  2. Max-Planck-Institut f. biol. Kybernetik, Spemannstr. 38, 72076, Germany

    B. Schölkopf

  3. AT&T Research, 101 Crawfords Corner Rd, PO 3030, 07733, Holmdel, USA

    V. Vapnik

Authors
  1. K. -R. Müller
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  2. A. J. Smola
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  3. G. Rätsch
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  4. B. Schölkopf
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  5. J. Kohlmorgen
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  6. V. Vapnik
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Müller, K.R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V. (1997). Predicting time series with support vector machines. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020283

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  • DOI: https://doi.org/10.1007/BFb0020283

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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