Mitigating the effects of measurement noise on Granger causality

Hariharan Nalatore, Mingzhou Ding, and Govindan Rangarajan
Phys. Rev. E 75, 031123 – Published 29 March 2007

Abstract

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.

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  • Received 13 September 2006

DOI:https://doi.org/10.1103/PhysRevE.75.031123

©2007 American Physical Society

Authors & Affiliations

Hariharan Nalatore* and Mingzhou Ding

  • The J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida 32611, USA

Govindan Rangarajan

  • Department of Mathematics, Indian Institute of Science, Bangalore 560 012, India

  • *Electronic address: hnalatore@bme.ufl.edu
  • Electronic address: mding@bme.ufl.edu
  • Electronic address: rangaraj@math.iisc.ernet.in

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Issue

Vol. 75, Iss. 3 — March 2007

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