Low-dimensional dynamics in observables from complex and higher-dimensional systems

M S  Baptista; I L  Caldas; M S  Baptista; C S  Baptista; A A  Ferreira; M V A P  Heller

doi:10.1016/S0378-4371(00)00448-9

Low-dimensional dynamics in observables from complex and higher-dimensional systems

M S Baptista, I L Caldas, M S Baptista, C S Baptista, A A Ferreira, M V A P Heller

Physics

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

We analyze fluctuating observables of high-dimensional systems as the New York Stock Market S&P 500 index, the amino-acid sequence in the M. genitalium DNA, the maximum temperature of the San Francisco Bay area, and the toroidal magneto plasma potential. The probability measures of these fluctuations are obtained by the statistical analysis of a rescaling combination of the first Poincare return time of a low-dimensional chaotic system. This result indicates that it is possible to use a measure of a low-dimensional chaotic attractor to describe a measure of these complex systems. Moreover, within this description we determine scaling power laws for average measurements of the analyzed fluctuations. (C) 2000 Elsevier Science B.V. All rights reserved.

Original language	English
Pages (from-to)	91-99
Number of pages	9
Journal	Physica. A, Statistical Mechanics and its Applications
Volume	287
Issue number	1-2
Early online date	30 Oct 2000
DOIs	https://doi.org/10.1016/S0378-4371(00)00448-9
Publication status	Published - 15 Nov 2000

Keywords

chaos
econophysics
stock market
DNA
turbulence
modeling
transport
weather

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10.1016/S0378-4371(00)00448-9

Cite this

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title = "Low-dimensional dynamics in observables from complex and higher-dimensional systems",

abstract = "We analyze fluctuating observables of high-dimensional systems as the New York Stock Market S&P 500 index, the amino-acid sequence in the M. genitalium DNA, the maximum temperature of the San Francisco Bay area, and the toroidal magneto plasma potential. The probability measures of these fluctuations are obtained by the statistical analysis of a rescaling combination of the first Poincare return time of a low-dimensional chaotic system. This result indicates that it is possible to use a measure of a low-dimensional chaotic attractor to describe a measure of these complex systems. Moreover, within this description we determine scaling power laws for average measurements of the analyzed fluctuations. (C) 2000 Elsevier Science B.V. All rights reserved.",

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T1 - Low-dimensional dynamics in observables from complex and higher-dimensional systems

AU - Baptista, M S

AU - Caldas, I L

AU - Baptista, M S

AU - Baptista, C S

AU - Ferreira, A A

AU - Heller, M V A P

PY - 2000/11/15

Y1 - 2000/11/15

N2 - We analyze fluctuating observables of high-dimensional systems as the New York Stock Market S&P 500 index, the amino-acid sequence in the M. genitalium DNA, the maximum temperature of the San Francisco Bay area, and the toroidal magneto plasma potential. The probability measures of these fluctuations are obtained by the statistical analysis of a rescaling combination of the first Poincare return time of a low-dimensional chaotic system. This result indicates that it is possible to use a measure of a low-dimensional chaotic attractor to describe a measure of these complex systems. Moreover, within this description we determine scaling power laws for average measurements of the analyzed fluctuations. (C) 2000 Elsevier Science B.V. All rights reserved.

AB - We analyze fluctuating observables of high-dimensional systems as the New York Stock Market S&P 500 index, the amino-acid sequence in the M. genitalium DNA, the maximum temperature of the San Francisco Bay area, and the toroidal magneto plasma potential. The probability measures of these fluctuations are obtained by the statistical analysis of a rescaling combination of the first Poincare return time of a low-dimensional chaotic system. This result indicates that it is possible to use a measure of a low-dimensional chaotic attractor to describe a measure of these complex systems. Moreover, within this description we determine scaling power laws for average measurements of the analyzed fluctuations. (C) 2000 Elsevier Science B.V. All rights reserved.

KW - chaos

KW - econophysics

KW - stock market

KW - DNA

KW - turbulence

KW - modeling

KW - transport

KW - weather

U2 - 10.1016/S0378-4371(00)00448-9

DO - 10.1016/S0378-4371(00)00448-9

M3 - Article

VL - 287

SP - 91

EP - 99

JO - Physica. A, Statistical Mechanics and its Applications

JF - Physica. A, Statistical Mechanics and its Applications

SN - 0378-4371

IS - 1-2

ER -

Low-dimensional dynamics in observables from complex and higher-dimensional systems

Abstract

Keywords

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