Wavelets; Time-frequency analysis; Mode extraction; EMD; Time series analysis
Abstract :
[en] This paper consists in the description and application of a method called wavelet-induced mode extraction (WIME) in the context of time-frequency analysis. WIME aims to extract the oscillating components that build amplitude modulated-frequency modulated signals. The essence of this technique relies on the successive extractions of the dominant ridges of wavelet-based time-frequency representations of the signal under consideration. Our tests on simulated examples indicate strong decomposition and reconstruction skills, trouble-free handling of crossing trajectories in the time-frequency plane, sharp performances in frequency detection in the case of mode-mixing problems, and a natural tolerance to noise. These results are compared with those obtained with empirical mode decomposition. We also show that WIME still gives meaningful results with real-life data, namely, the Oceanic Niño Index.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others Mathematics Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Author, co-author :
Deliège, Adrien ; Université de Liège > Département de mathématique > Analyse - Analyse fonctionnelle - Ondelettes
Nicolay, Samuel ; Université de Liège > Département de mathématique > Analyse - Analyse fonctionnelle - Ondelettes
Language :
English
Title :
Extracting oscillating components from nonstationary time series: A wavelet-induced method
Publication date :
11 September 2017
Journal title :
Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
ISSN :
1063-651X
eISSN :
1095-3787
Publisher :
American Physical Society, College Park, United States - Maryland
Volume :
96
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
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