Paper abstractAssessing Nonlinear Granger Causality from Multivariate Time SeriesXiaohai Sun - Max Planck Institute for Biological Cybernetics, GermanySession: Kernel Methods Springer Link: http://dx.doi.org/10.1007/978-3-540-87481-2_29 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. |