2022/04/19 by Morel, Rudy, Rochette, Gaspar, Leonarduzzi, Roberto +2 · 1 citation
#Data Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Computer and information sciences #FOS: Economics and business #FOS: Electrical engineering #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical Finance (q-fin.MF) #Signal Processing (eess.SP) #Statistics and Probability (physics.data-an) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2204.10177
We introduce the wavelet scattering spectra which provide non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at each scale. Dependencies across scales are captured by the joint correlation across time and scales of wavelet coefficients and their modulus. This correlation matrix is nearly diagonalized by a second wavelet transform, which defines the scattering spectra. We show that this vector of moments characterizes a wide range of non-Gaussian properties of multi-scale processes. We prove that self-similar processes have scattering spectra which are scale invariant. This property can be tested statistically on a single realization and defines a class of wide-sense self-similar processes. We build maximum entropy models conditioned by scattering spectra coefficients, and generate new time-series with a microcanonical sampling algorithm. Applications are shown for highly non-Gaussian financial and turbulence time-series.