Estimating granger causality from fourier and wavelet transforms of time series data
Estimating granger causality from fourier and wavelet transforms of time series data
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DOI：
10.1103/physrevlett.100.018701
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发表时间：
2008-01-11
影响因子：
8.6
通讯作者：
Ding, Mingzhou
中科院分区：
文献类型：
--
作者：
Dhamala, Mukeshwar;Rangarajan, Govindan;Ding, Mingzhou
许多科学和工程领域的实验数据都是以时间序列的形式出现的。基于傅立叶变换和小波变换的非参数方法被广泛用于研究这些时间序列数据的谱特征。在这里，我们扩展了非参数谱方法的框架，以包括Granger因果谱的估计，以评估方向性影响。我们使用由相互作用的动力系统组成的网络模型的合成数据来说明所提出的方法的实用性。
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.