Kernel Methods for Detecting the Direction of Time Series

Peters, J; Janzing, D; Gretton, A; Schölkopf, B

doi:10.1007/978-3-642-01044-6_5

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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アイテムのパーマリンク: https://hdl.handle.net/11858/00-001M-0000-0013-C198-B 版のパーマリンク: https://hdl.handle.net/21.11116/0000-0002-94E3-6

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作成者:
Peters, J^{1, 2}, 著者
Janzing, D^{1, 2}, 著者
Gretton, A^{1, 2}, 著者
Schölkopf, B^{1, 2}, 著者

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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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要旨: 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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日付: オンライン出版: 2009-07出版: 2010

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識別子（DOI, ISBNなど）: DOI: 10.1007/978-3-642-01044-6_5
BibTex参照ID: 5662

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出版物名: 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

種別: 学術雑誌

著者・編者:
Fink, A, 編集者
Lausen, B, 編集者
Seidel, W, 編集者
Ultsch, A, 編集者

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出版社, 出版地: Berlin, Germany : Springer

ページ: - 巻号: - 通巻号: - 開始・終了ページ: 57 - 66 識別子（ISBN, ISSN, DOIなど）: ISBN: 978-3-642-01044-6

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