Scale Dependencies and Self-Similar Models with Wavelet Scattering Spectra
This provides a novel method for analyzing and generating non-Gaussian multi-scale time-series, addressing challenges in fields like finance and turbulence, but it is incremental as it builds on existing wavelet and maximum entropy frameworks.
The paper tackles the problem of modeling non-Gaussian time-series with stationary increments by introducing wavelet scattering spectra, which capture scale dependencies and characterize non-Gaussian properties, and shows that these spectra are scale-invariant for self-similar processes, enabling applications in finance and turbulence.
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.