Author： Rinn Philip Stepanov Yuriy Peinke Joachim Guhr Thomas Schäfer Rudi
Publisher： Edp Sciences
E-ISSN： 1286-4854|110|6|68003-68003
ISSN： 0295-5075
Source： EPL (EUROPHYSICS LETTERS), Vol.110, Iss.6, 2015-07, pp. : 68003-68003
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Abstract
We propose a combination of cluster analysis and stochastic process analysis to characterize high-dimensional complex dynamical systems by few dominating variables. As an example, stock market data are analyzed for which the dynamical stability as well as transitions between different stable states are found. This combined method allows especially to set up new criteria for merging clusters to uncover dynamically distinct states. The low-dimensional approach allows to recover the high-dimensional fixed points of the system by means of an optimization procedure.
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