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  Kernel Methods for Detecting the Direction of Time Series

Peters, J., Janzing, D., Gretton, A., & Schölkopf, B. (2010). Kernel Methods for Detecting the Direction of Time Series. Advances in Data Analysis, Data Handling and Business Intelligence, 57-66.

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 Urheber:
Peters, J1, Autor           
Janzing, D2, Autor           
Gretton, A1, Autor           
Schölkopf, B1, Autor           
Fink, Herausgeber
A., Herausgeber
Lausen, B., Herausgeber
Seidel, W., Herausgeber
Ultsch, A., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: We propose two kernel based methods for detecting the time direction in empirical time series. First we apply a Support Vector Machine on the finite-dimensional distributions of the time series (classification method) by embedding these distributions into a Reproducing Kernel Hilbert Space. For the ARMA method we fit the observed data with an autoregressive moving average process and test whether the regression residuals are statistically independent of the past values. Whenever the dependence in one direction is significantly weaker than in the other we infer the former to be the true one. Both approaches were able to detect the direction of the true generating model for simulated data sets. We also applied our tests to a large number of real world time series. The ARMA method made a decision for a significant fraction of them, in which it was mostly correct, while the classification method did not perform as well, but still exceeded chance level.

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 Datum: 2010
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
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 Identifikatoren: ISBN: 978-3-642-01044-6
URI: http://gfkl2008.hsu-hh.de/
DOI: 10.1007/978-3-642-01044-6_5
BibTex Citekey: 5662
 Art des Abschluß: -

Veranstaltung

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Titel: 32nd Annual Conference of the Gesellschaft für Klassifikation e.V. (GfKl 2008)
Veranstaltungsort: Hamburg, Germany
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Titel: Advances in Data Analysis, Data Handling and Business Intelligence
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 57 - 66 Identifikator: -