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Conference Paper

Predicting time series with support vectur machines

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Smola AJ, Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Müller, K.-R., Smola AJ, Rätsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vectur machines. 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland, 999-1004.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E9D4-0
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.