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

We analyze fluctuating observables of high-dimensional systems as the New Y ork Stock Market S&P 500 index, the amino-acid sequence in the M. genitaliu m DNA, the maximum temperature of the San Francisco Bay area, and the toroi dal magneto plasma potential. The probability measures of these fluctuation s are obtained by the statistical analysis of a rescaling combination of th e first Poincare return time of a low-dimensional chaotic system. This resu lt indicates that it is possible to use a measure of a low-dimensional chao tic attractor to describe a measure of these complex systems. Moreover, wit hin this description we determine scaling power laws for average measuremen ts of the analyzed fluctuations. (C) 2000 Elsevier Science B.V. All rights reserved.