Modeling Complex Phenomena Using Multiscale Time Sequences

Abstract

The purpose of the work funded by this grant was to study and model complex phenomena by using an approach that makes use of multiscale time sequences. The underlying idea is that a complex dynamical system can be better interpreted and modeled by taking into account how its dynamics behaves at different scales and how these scales relate to each other. This can be done by combining a set statistical fractal measures based on Hurst and Holder exponents, auto-regressive methods and Fourier and wavelet decomposition methods. The applications for this technology include mathematical algorithms to model and predict geophysical phenomena such as climate and weather patterns as well as biological/biomedical phenomena such as, for example, for the treatment of injured individuals.

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Document Details

Document Type
Technical Report
Publication Date
Aug 24, 2009
Accession Number
ADA508771

Entities

People

  • Nicola Scafetta
  • Richard E Moon

Organizations

  • Duke University

Tags

Communities of Interest

  • Biomedical
  • Space

DTIC Thesaurus Topics

  • Chemical Reactions
  • Climate Change
  • Complex Systems
  • Dynamics
  • Energy Transfer
  • Engineering
  • Magnetic Fields
  • Mathematics
  • Neurodegenerative Diseases
  • Physics
  • Probability
  • Probability Distributions
  • Respiration
  • Sequences
  • Students
  • Surface Temperature
  • White Noise

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • Biotechnology