Analytic wavelets for multivariate time series analysis

Irène Gannaz; Sophie Achard; Marianne Clausel; François Roueff

doi:10.1117/12.2272928

24 August 2017 Analytic wavelets for multivariate time series analysis

Irène Gannaz, Sophie Achard, Marianne Clausel, François Roueff

Proceedings Volume 10394, Wavelets and Sparsity XVII; 103941X (2017) https://doi.org/10.1117/12.2272928
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

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.

Citation Download Citation

Irène Gannaz, Sophie Achard, Marianne Clausel, and François Roueff "Analytic wavelets for multivariate time series analysis", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941X (24 August 2017); https://doi.org/10.1117/12.2272928

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