Assessing Cross-Dependencies Using Bivariate Multifractal Analysis
Herwig Wendt, Roberto F. Leonarduzzi, Patrice Abry, Stéphane G. Roux, Stéphane Jaffard, Stéphane Seuret
Abstract
Multifractal analysis, notably with its recent wavelet-leader based formulation, has nowadays become a reference tool to characterize scale-free temporal dynamics in time series. It proved successful in numerous applications very diverse in nature. However, such successes remained restricted to univariate analysis while many recent applications call for the joint analysis of several components. Surprisingly, multivariate multifractal analysis remained mostly overlooked. The present contribution aims at defining a wavelet-leader based framework for multivariate multifractal analysis and at studying its properties and estimation performance. To better understand what properties of multivariate data are actually captured in multivariate multifractal analysis, a multivariate multifractal model is used as representative paradigm and permits to show that multivariate multifractal analysis puts in evidence transient and local dependencies that are not well quantified or even evidenced by the classical Pearson correlation coefficient.
BibTeX
@inproceedings{icassp2018_assessingcrossde,
title = {Assessing Cross-Dependencies Using Bivariate Multifractal Analysis},
author = {Herwig Wendt and Roberto F. Leonarduzzi and Patrice Abry and Stéphane G. Roux and Stéphane Jaffard and Stéphane Seuret},
booktitle = {ICASSP 2018},
year = {2018}
}