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Cited by 710

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Distinguishing Noise from Chaos

Open access
Rosso, O. A., Larrondo, H. A., Martin, M. T., Plastino, A., Fuentes, M. A.·2007·Physical Review Letters

Abstract

Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise.

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Metadata

Authors
Rosso, O. A., Larrondo, H. A., Martin, M. T., Plastino, A., & Fuentes, M. A.
Venue
Physical Review Letters
Published
2007-10-12
Volume
99
Issue
15
Pages
154102-154102
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W2030486754
Cited by
710
References
15
FWCI
10.87
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Distinguishing Noise from Chaos. https://app.cochat.ai/papers/10.1103/physrevlett.99.154102
The paper
Rosso, O. A., Larrondo, H. A., Martin, M. T., Plastino, A., & Fuentes, M. A. (2007). Distinguishing Noise from Chaos. Physical Review Letters. https://doi.org/10.1103/physrevlett.99.154102

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.