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Analytic wavelets for multivariate time series analysis
Author(s): Irène Gannaz; Sophie Achard; Marianne Clausel; François Roueff
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Paper Abstract

Many applications fields deal with multivariate long-memory time series. A challenge is to estimate the long-memory properties together with the coupling between the time series. Real wavelets procedures present some limitations due to the presence of phase phenomenons. A perspective is to use analytic wavelets to recover jointly long-memory properties, modulus of long-run covariance between time series and phases. Approximate wavelets Hilbert pairs of Selesnick (2002) fullfilled some of the required properties. As an extension of Selesnick (2002)’s work, we present some results about existence and quality of these approximately analytic wavelets.

Paper Details

Date Published: 24 August 2017
PDF: 8 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941X (24 August 2017); doi: 10.1117/12.2272928
Show Author Affiliations
Irène Gannaz, Univ. de Lyon, CNRS, INSA de Lyon, Institute Camille Jordan (France)
Sophie Achard, CNRS, Univ. Grenoble Alpes, GIPSA-lab (France)
Marianne Clausel, Univ. Grenoble Alpes, LJK (France)
François Roueff, LTCI, Télécom ParisTech, Univ. Paris-Saclay (France)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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