Multifractal perturbations to multiplicative cascades promote multifractal nonlinearity with asymmetric spectra
- 1. Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Nebraska 68182, USA
- 2. Department of Psychology, State University of New York at New Paltz, New Paltz, New York 12561, USA
Description
Biological and psychological processes have been conceptualized as emerging from intricate multiplicative interactions among component processes across various spatial and temporal scales. Among the statistical models employed to approximate these intricate nonlinear interactions across scales, one prominent framework is that of cascades. Despite decades of empirical work using multifractal formalisms, several fundamental questions persist concerning the proper interpretations of multifractal evidence of nonlinear cross-scale interactivity. Does multifractal spectrum width depend on multiplicative interactions, constituent noise processes participating in those interactions, or both? We conducted numerical simulations of cascade time series featuring component noise processes characterizing a range of nonlinear temporal correlations: nonlinearly multifractal, linearly multifractal (obtained via the iterative amplitude adjusted wavelet transform of nonlinearly multifractal), phase-randomized linearity (obtained via the iterative amplitude adjustment Fourier transform of nonlinearly multifractal), and phase and amplitude randomized (obtained via shuffling of nonlinearly multifractal). Our findings show that the multiplicative interactions coordinate with the nonlinear temporal correlations of noise components to dictate emergent multifractal properties. Multiplicative cascades with stronger nonlinear temporal correlations make multifractal spectra more asymmetric with wider left sides. However, when considering multifractal spectral differences between the original and surrogate time series, even multiplicative cascades produce multifractality greater than in surrogate time series, even with linearized multifractal noise components. In contrast, additivity among component processes leads to a linear outcome. These findings provide a robust framework for generating multifractal expectations for biological and psychological models in which cascade dynamics flow from one part of an organism to another.
Additional details
Identifiers
- DOI
- 10.1103/PhysRevE.109.064212;
- Crossref Funder ID
- 10.13039/100000057;
Publishing Information
- Journal Title
- Physical Review E
- Journal Volume
- 109
- Journal Issue
- 6
- Journal Page Range
- 26 pgs.
- ISSN
- 1089-3787
INIS
- Country of Publication
- United States
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING; S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- AMPLITUDES; ASYMMETRY; COMPUTERIZED SIMULATION; COORDINATES; CORRELATIONS; DISTURBANCES; DYNAMICAL SYSTEMS; FOURIER TRANSFORMATION; FRACTALS; INTERACTIONS; ITERATIVE METHODS; NOISE; NONLINEAR PROBLEMS; PERTURBATION THEORY; RANDOMNESS; SPECTRA
- Descriptors DEC
- CALCULATION METHODS; INTEGRAL TRANSFORMATIONS; SIMULATION; TRANSFORMATIONS
Optional Information
- Copyright
- ©2024 American Physical Society
- Contract/Grant/Project number
- P20GM109090
- Notes
- Contact Email: Contact author: mmangalam@unomaha.edu; Contact Email: Contact author: keltystd@newpaltz.edu; Record automatically processed
- Funding organization
- National Institute of General Medical Sciences