Intermittent process analysis with scattering moments

B. Y.Joan Bruna, S. Téphane Mallat, Emmanuel Bacry, Jean F.Rançois Muzy

Research output: Contribution to journalArticlepeer-review


Scattering moments provide nonparametric models of random processes with stationary increments. They are expected values of random variables computed with a nonexpansive operator, obtained by iteratively applying wavelet transforms and modulus nonlinearities, which preserves the variance. First- and second-order scattering moments are shown to characterize intermittency and self-similarity properties of multiscale processes. Scattering moments of Poisson processes, fractional Brownian motions, Lévy processes and multifractal random walks are shown to have characteristic decay. The Generalized Method of Simulated Moments is applied to scattering moments to estimate data generating models. Numerical applications are shown on financial time-series and on energy dissipation of turbulent flows.

Original languageEnglish (US)
Pages (from-to)323-351
Number of pages29
JournalAnnals of Statistics
Issue number1
StatePublished - Feb 1 2015


  • Generalized method of moments
  • Intermittency
  • Multifractal
  • Spectral analysis
  • Wavelet analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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