Predicting time series with support vector machines View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1997

AUTHORS

K. -R. Müller , A. J. Smola , G. Rätsch , B. Schölkopf , J. Kohlmorgen , V. Vapnik

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%. More... »

PAGES

999-1004

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0020283

DOI

http://dx.doi.org/10.1007/bfb0020283

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042514609


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