Estimating Granger causality from Fourier and wavelet transforms of time series data

TL;DR

This paper extends nonparametric spectral methods using Fourier and wavelet transforms to estimate Granger causality spectra, enabling analysis of directional influences in time series data across various scientific fields.

Contribution

It introduces a novel framework for nonparametric Granger causality estimation using Fourier and wavelet transforms, applicable to complex dynamical systems.

Findings

Effective estimation of Granger causality spectra demonstrated on synthetic network data

Framework captures directional influences accurately in interacting dynamical systems

Method enhances spectral analysis of time series with nonparametric approaches

Abstract

Experiments in many fields of science and engineering yield data in the form of time series. The Fourier and wavelet transform-based nonparametric methods are used widely to study the spectral characteristics of these time series data. Here, we extend the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directional influences. We illustrate the utility of the proposed methods using synthetic data from network models consisting of interacting dynamical systems.