Predicting Time Series with Support Vector Machines

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Predicting Time Series with Support Vector Machines (1997)

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by Muller Smola , A. J. Smola , G. Ratsch , B. Scholkopf , J. Kohlmorgen , V. Vapnik

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189 - 13 self

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@MISC{Smola97predictingtime,
    author = {Muller Smola and A. J. Smola and G. Ratsch and B. Scholkopf and J. Kohlmorgen and V. Vapnik},
    title = {Predicting Time Series with Support Vector Machines},
    year = {1997}
}

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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 ffl 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%. 1 Introduction Support Vector Machines have become a subject of intensive study (see e.g. [3, 14]). They have been applied successfully to classification tasks as OCR [14, 11] and more recently also to regression [5, 15]. In this contribution we use Support Vector Machines in the field of time series prediction and we find that they show an excel...

Keyphrases

support vector machine time series time series prediction known result different cost function introduction support vector machine intensive study mackey glass equation santa fe competition uniform noise robust loss function radial basis function network support vector regularization parameter case support vector machine excellent performance ffl insensitive loss support vector approach classification task

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