论文标题
当地的格兰杰因果关系
Local Granger Causality
论文作者
论文摘要
Granger因果关系是基于媒介自动追溯预测的因果影响的统计概念。对于高斯变量，它等效于转移熵，这是一个信息理论的衡量时间定向信息传递之间的信息在共同依赖过程之间传递。我们利用这种等价性并准确计算“本地Granger因果关系”，即高斯过程中每个离散时间点的信息传输的曲线；在此框架中，Granger因果关系是其本地版本的平均值。我们的方法提供了一种强大且计算快速的方法，可以遵循信息传递的线性随机过程的时间历史以及在高斯近似中研究的非线性复杂系统的时间历史。
Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear complex systems studied in the Gaussian approximation.
