24 August 2017 Analytic wavelets for multivariate time series analysis
Author Affiliations +
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Irène Gannaz, Irène Gannaz, Sophie Achard, Sophie Achard, Marianne Clausel, Marianne Clausel, François Roueff, François Roueff, } "Analytic wavelets for multivariate time series analysis", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941X (24 August 2017); doi: 10.1117/12.2272928; https://doi.org/10.1117/12.2272928
PROCEEDINGS
8 PAGES


SHARE
Back to Top