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Predicting time series with support vector machines

1997

Conference Paper

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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
Pages: 999-1004
Year: 1997
Month: October
Day: 0
Editors: Sch{\"o}lkopf, B. , C.J.C. Burges, A.J. Smola
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

BibTex

@inproceedings{790,
  title = {Predicting time series with support vector 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.},
  booktitle = {Artificial Neural Networks: ICANN'97},
  pages = {999-1004},
  editors = {Sch{\"o}lkopf, B. , C.J.C. Burges, A.J. Smola},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {1997},
  month_numeric = {10}
}