A Gaussian process regression approach for testing Granger causality between time series data

Granger causality considers the question of whether two time series exert causal influences on each other. Causality testing usually relies on prediction, i.e., if the prediction error of the first time series is reduced by taking measurements from the second one into account, then the latter is said to have a causal influence on the former. In this paper, a nonparametric framework based on functional estimation is proposed. Nonlinear prediction is performed via the Bayesian paradigm, using Gaussian processes. Some experiments illustrate the efficiency of the approach.