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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: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), Helmut-Schmidt-University, Hamburg, July 16-18, 2008, 57-66. doi:10.1007/978-3-642-01044-6_5.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C198-B Version Permalink: https://hdl.handle.net/21.11116/0000-0002-94E3-6
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-642-01044-6_5.pdf (Publisher version)
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 Creators:
Peters, J1, 2, Author           
Janzing, D1, 2, Author           
Gretton, A1, 2, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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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 Dates: 2009-072010
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-01044-6_5
BibTex Citekey: 5662
 Degree: -

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Title: 32nd Annual Conference of the Gesellschaft für Klassifikation e.V. (GfKl 2008)
Place of Event: Hamburg, Germany
Start-/End Date: 2008-07-16 - 2008-07-18

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Title: Advances in Data Analysis, Data Handling and Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), Helmut-Schmidt-University, Hamburg, July 16-18, 2008
Source Genre: Journal
 Creator(s):
Fink, A, Editor
Lausen, B, Editor
Seidel, W, Editor
Ultsch, A, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 57 - 66 Identifier: ISBN: 978-3-642-01044-6