Mitigating the effects of measurement noise on Granger causality
Mitigating the effects of measurement noise on Granger causality
复制标题
DOI：
10.1103/physreve.75.031123
复制
发表时间：
2007-03-01
影响因子：
2.4
通讯作者：
Rangarajan, Govindan
中科院分区：
文献类型：
--
作者：
Nalatore, Hariharan;Ding, Mingzhou;Rangarajan, Govindan
在过去几年中，计算双变量实验观察时间序列之间的格兰杰因果关系受到越来越多的关注。如果正确估计，这种因果关系可以对所研究的系统的动态组织产生重要的见解。由于实验测量不可避免地受到噪声污染，因此了解此类噪声对格兰杰因果关系估计的影响非常重要。本文的第一个目标是对该问题进行解析和数值分析。具体来说，我们表明，由于噪声污染，（1）两个测量变量之间可能会出现虚假因果关系，（2）真正的因果关系可能会被抑制。本文的第二个目标是提供一种去噪策略来缓解这个问题。具体来说，我们提出了一种基于卡尔曼滤波器理论和期望最大化算法相结合的去噪算法。数值例子用于证明去噪方法的有效性。
Computing Granger causal relations among bivariate experimentally observed time series has received increasing attention over the past few years. Such causal relations, if correctly estimated, can yield significant insights into the dynamical organization of the system being investigated. Since experimental measurements are inevitably contaminated by noise, it is thus important to understand the effects of such noise on Granger causality estimation. The first goal of this paper is to provide an analytical and numerical analysis of this problem. Specifically, we show that, due to noise contamination, (1) spurious causality between two measured variables can arise and (2) true causality can be suppressed. The second goal of the paper is to provide a denoising strategy to mitigate this problem. Specifically, we propose a denoising algorithm based on the combined use of the Kalman filter theory and the expectation-maximization algorithm. Numerical examples are used to demonstrate the effectiveness of the denoising approach.