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

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