Surrogate-based test for Granger causality

A novel approach for testing the presence of Granger causality between two time series is proposed. The residue of the destination signal after self-prediction is computed, after which a crossprediction of the source signal over this residue is examined. In the absence of causality, there should be no cross-predictive power, due to which the performance of the cross-prediction system can be used as an indication of causality. The proposed approach uses the surrogate data method, and implements the selfand crossprediction systems as feedforward neural networks. It is tested on synthetic examples, and a sensitivity analysis demonstrates the robustness of the approach.