Instantaneous Granger Causality with the Hilbert-Huang Transform. (20th February 2013)

• Record Type: Journal Article
• Authors: Rodrigues, João; Andrade, Alexandre
• Other Names: Tsihrintzis G. A. Academic Editor.; Wicks M. Academic Editor.
• Is Part Of: ISRN signal processing. Volume 2013(2013)
• Publication Date: 2013-02-20
• Subjects: Signal processing -- Periodicals; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2013/374064
• Languages: English
• ISSNs: 2090-5041

Abstract

Abstract : Current measures of causality and temporal precedence have limited frequency and time resolution and therefore may not be viable in the detection of short periods of causality in specific frequencies. In addition, the presence of nonstationarities hinders the causality estimation of current techniques as they are based on Fourier transforms or autoregressive model estimation. In this work we present a combination of techniques to measure causality and temporal precedence between stationary and nonstationary time series, that is sensitive to frequency-specific short episodes of causality. This methodology provides a highly informative time-frequency representation of causality with existing causality measures. This is done by decomposing each time series into intrinsic oscillatory modes with an empirical mode decomposition algorithm and, subsequently, calculating their complex Hilbert spectrum. At each time point the cross-spectrum is calculated between time series and used to measure coherency and compute the transfer function and error covariance matrices using the Wilson-Burg method for spectral factorization. The imaginary part of coherency can then be computed as well as several Granger causality measures in the previous matrices. This work covers the most important theoretical background of these techniques and tries to prove the usefulness of this new approach while pointing out some of its qualities and drawbacks.