- Recherche et sélection de publications
|
Analytic wavelets for multivariate time series analysis
- Irène Gannaz, Sophie Achard #1, Marianne Clausel, François Roueff
-
| #1 |
Grenoble Images Parole Signal Automatique (GIPSA-lab)
- CNRS : UMR5216
- Université Joseph Fourier - Grenoble I
- Université Pierre Mendès-France - Grenoble II
- Université Stendhal - Grenoble III
- Institut Polytechnique de Grenoble - Grenoble Institute of Technology
|
- References
- SPIE, San Diego, USA, August 2017,
- 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.
- Keywords
- Category
- Paper in proceedings
- Research Area(s)
- Computer Science/Signal and Image Processing
Statistics/Applications
Statistics/Methodology
- Identifier(s)
-
Bibliographic key spie2017-GACR
- Export
-
- Last update
- on september 05, 2017 by Francois Roueff
|
