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Conference Paper

Assessing Nonlinear Granger Causality from Multivariate Time Series

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Sun,  X
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C72B-B
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.