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Predicting time series with support vectur machines

1997

Conference Paper

ei


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%.

Author(s): Müller, K-R. and Smola, AJ. and Rätsch, G. and Schölkopf, B. and Kohlmorgen, J. and Vapnik, V.
Book Title: Artificial neural networks: ICANN ’97
Journal: 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland
Pages: 999-1004
Year: 1997
Month: October
Day: 0
Editors: W Gerstner and A Germond and M Hasler and J-D Nicoud
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/BFb0020283
Event Name: 7th International Conference on Artificial Neural Networks
Event Place: Lausanne, Switzerland

Address: Berlin, Germany
Digital: 0
ISBN: 3-540-63631-5
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{416,
  title = {Predicting time series with support vectur machines},
  author = {M{\"u}ller, K-R. and Smola, AJ. and R{\"a}tsch, G. and Sch{\"o}lkopf, B. and Kohlmorgen, J. and Vapnik, V.},
  journal = {7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland},
  booktitle = {Artificial neural networks: ICANN '97},
  pages = {999-1004},
  editors = {W Gerstner and  A Germond and M Hasler and J-D Nicoud},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {1997},
  month_numeric = {10}
}