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Bivariate Empirical Mode Decomposition

Abstract : The Empirical Mode Decomposition (EMD) has been introduced quite recently to adaptively decompose nonstationary and/or nonlinear time series. The method being initially limited to real-valued time series, we propose here an extension to bivariate (or complex-valued) time series which generalizes the rationale underlying the EMD to the bivariate framework. Where the EMD extracts zero-mean oscillating components, the proposed bivariate extension is designed to extract zero-mean rotating components. The method is illustrated on a real-world signal and properties of the output components are discussed. Free Matlab/C codes are available at http://perso.ens-lyon.fr/patrick.flandrin.
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https://hal-ens-lyon.archives-ouvertes.fr/ensl-00137611
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Submitted on : Tuesday, March 20, 2007 - 5:20:42 PM
Last modification on : Saturday, September 11, 2021 - 3:17:04 AM
Long-term archiving on: : Tuesday, April 6, 2010 - 10:56:37 PM

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  • HAL Id : ensl-00137611, version 1

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Gabriel Rilling, Patrick Flandrin, Paulo Gonçalves, Jonathan M. Lilly. Bivariate Empirical Mode Decomposition. 2007. ⟨ensl-00137611⟩

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