Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

A direct method to detect deterministic and stochastic properties of data


Prado,  Thiago Lima
External Organizations;

Boaretto,  Bruno Rafael Reichert
External Organizations;

Corso,  Gilberto
External Organizations;

dos Santos Lima,  Gustavo Zampier
External Organizations;


Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Lopes,  Sergio Roberto
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)

(Publisher version), 4MB

Supplementary Material (public)
There is no public supplementary material available

Prado, T. L., Boaretto, B. R. R., Corso, G., dos Santos Lima, G. Z., Kurths, J., Lopes, S. R. (2022): A direct method to detect deterministic and stochastic properties of data. - New Journal of Physics, 24, 033027.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_27033
A fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals.