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  Assessing Nonlinear Granger Causality from Multivariate Time Series

Sun, X. (2008). Assessing Nonlinear Granger Causality from Multivariate Time Series. In W. Daelemans, B. Goethals, & K. Morik (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008 (pp. 440-455). Berlin, Germany: Springer.

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Sun, X1, 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: A straightforward nonlinear extension of Granger’s concept of causality in the kernel framework is suggested. The kernel-based approach to assessing nonlinear Granger causality in multivariate time series enables us to determine, in a model-free way, whether the causal relation between two time series is present or not and whether it is direct or mediated by other processes. The trace norm of the so-called covariance operator in feature space is used to measure the prediction error. Relying on this measure, we test the improvement of predictability between time series by subsampling-based multiple testing. The distributional properties of the resulting p-values reveal the direction of Granger causality. Experiments with simulated and real-world data show that our method provides encouraging results.

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 Dates: 2008-09
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-540-87481-2_29
BibTex Citekey: 5254
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Title: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008)
Place of Event: Antwerpen, Belgium
Start-/End Date: 2008-09-15 - 2008-09-19

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Title: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008
Source Genre: Proceedings
 Creator(s):
Daelemans, W, Editor
Goethals, B, Editor
Morik, K, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 440 - 455 Identifier: ISBN: 978-3-540-87480-5

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 5212 Sequence Number: - Start / End Page: - Identifier: -