Bootstrapping Multifractals: Surrogate Data from Random Cascades on Wavelet Dyadic Trees

Milan Paluš
Phys. Rev. Lett. 101, 134101 – Published 25 September 2008

Abstract

A method for random resampling of time series from multiscale processes is proposed. Bootstrapped series—realizations of surrogate data obtained from random cascades on wavelet dyadic trees—preserve the multifractal properties of input data, namely, interactions among scales and nonlinear dependence structures. The proposed approach opens the possibility for rigorous Monte Carlo testing of nonlinear dependence within, with, between, or among time series from multifractal processes.

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  • Received 30 March 2007

DOI:https://doi.org/10.1103/PhysRevLett.101.134101

©2008 American Physical Society

Authors & Affiliations

Milan Paluš*

  • Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod vodárenskou věží 2, 182 07 Prague 8, Czech Republic

  • *mp@cs.cas.cz

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Vol. 101, Iss. 13 — 26 September 2008

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