Long range correlations in dynamical systems and in observed data

4 Sep 2017, 17:35
30m
56 ()

56

oral Session 3

Speaker

Holger Kantz (Max Planck Institute for the Physics of Complex Systems)

Description

Long range temporal correlations (LRC) in noise-like signals can be detected through the scaling behaviour of the mean squared displacement (MSD) of the pathes which one obtains by integrating over the signal. Detrended fluctuation analysis has become a standard tool which beyond a simple MSD analysis is able to remove the effects of trends on the signal. In the first part of this talk we present a sketch of theoretical considerations which give a better justification for DFA than it has been presented before. In the second part, we show the consequences of LRC on the convergence of time averages, on the probability for large deviations, and for the estimation of trends. In order to transfer these findings to real world data, we need paradigmatic data models with a minimum of free parameters.

Primary author

Holger Kantz (Max Planck Institute for the Physics of Complex Systems)

Co-authors

Katja Polotzek (Max Planck Institute for the Physics of Complex Systems) Marc Hoell (Max Planck Institute for the Physics of Complex Systems) Mozhdeh Massah (Max Planck Institute for the Physics of Complex Systems)

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