Wavelet techniques for processing multifractal signals

Multifractal analysis has been successfully applied to many types of signals, starting from turbulence velocity fields, and then extending to traffic, economic and medecine signals. In each case, it supplies new parameters which allow for a fine classification of the families of signals considered. Wavelet methods were introduced in order to obtain fast and robust numerical algorithms in this field. We will present the general purposes and techniques of multifractal analysis, and then we will focus on the recently introduced 'wavelet leaders' method, which yields the fastest and most accurate numerical methods. Finally, we will show that it yields an efficient tool to perform classification and model selection for several types of signals.