We present a direct method of calculation of the multifractal spectrum from the wavelet decomposition. Information pertinent to singular structures in time series is captured by the WTMM method and the local effective Hoelder exponent is evaluated locally for each singular point of the time series. The direct multifractal spectrum is obtained from the scaling of the histograms of the local effective Hoelder exponent. In addition, we illustrate the possibility of estimation of the spectrum from the entire continuous wavelet transform
Multifractal analysis has become a standard signal processing tool, for which a promising new formul...
Multifractal analysis studies signals, functions, images or fields via the fluctuations of their loc...
In this article fractal scale exponent estimation approach using Continuous Wavelet Transform is con...
We present a robust method of estimating the effective strength of singularities (the effective Hoel...
We present a robust method of estimating an effective H\"older exponent locally at an arbitrary reso...
The properties of several multifractal formalisms based on wavelet coefficients are compared from bo...
The multifractal formalism was introduced in the context of fully-developed turbulence data analysis...
The robustness of two widespread multifractal analysis methods, one based on detrended fluctuation a...
International audienceMultifractal analysis has become a powerful signal processing tool that charac...
A technique termed gradual multifractal reconstruction (GMR) is formulated. A continuum is defined f...
We show how wavelet techniques allow to derive irregularity properties of functions on two particula...
This article is dedicated to eliminate financial time series multifractal research method which is b...
International audienceIn this course, we give the basics of the part of multifractal theory that int...
International audienceThe multifractal formalism for singular measures is revisited using the wavele...
Multifractal description of signals has been developed during the last 20 years, mainly in the fully...
Multifractal analysis has become a standard signal processing tool, for which a promising new formul...
Multifractal analysis studies signals, functions, images or fields via the fluctuations of their loc...
In this article fractal scale exponent estimation approach using Continuous Wavelet Transform is con...
We present a robust method of estimating the effective strength of singularities (the effective Hoel...
We present a robust method of estimating an effective H\"older exponent locally at an arbitrary reso...
The properties of several multifractal formalisms based on wavelet coefficients are compared from bo...
The multifractal formalism was introduced in the context of fully-developed turbulence data analysis...
The robustness of two widespread multifractal analysis methods, one based on detrended fluctuation a...
International audienceMultifractal analysis has become a powerful signal processing tool that charac...
A technique termed gradual multifractal reconstruction (GMR) is formulated. A continuum is defined f...
We show how wavelet techniques allow to derive irregularity properties of functions on two particula...
This article is dedicated to eliminate financial time series multifractal research method which is b...
International audienceIn this course, we give the basics of the part of multifractal theory that int...
International audienceThe multifractal formalism for singular measures is revisited using the wavele...
Multifractal description of signals has been developed during the last 20 years, mainly in the fully...
Multifractal analysis has become a standard signal processing tool, for which a promising new formul...
Multifractal analysis studies signals, functions, images or fields via the fluctuations of their loc...
In this article fractal scale exponent estimation approach using Continuous Wavelet Transform is con...