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 accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Permutation Entropy: A Natural Complexity Measure for Time Series
Bandt, C., & Pompe, B. (2002) · Physical Review Letters · doi:10.1103/physrevlett.88.174102 · Cited by 4,867
1 A Statistical Measure of ComplexityPreprint
López‐Ruiz, R., Mancini, H., & Calbet, X. (1995) · Cited by 939
A Statistical Measure of Complexity
López-Ruiz, R., Mancini, H. L., & Calbet, X. (1995) · Physics Letters A · doi:10.1016/0375-9601(95)00867-5 · Cited by 920
A Study in the Analysis of Stationary Time Series
Bartlett, M. S., & Wold, H. (1955) · Econometrica · doi:10.2307/1907883 · Cited by 745
A Study in Analysis of Stationary Time Series.
N., J., & Wold, H. (1939) · Journal of the Royal Statistical Society · doi:10.2307/2980009 · Cited by 574
Deterministic Chaos: An Introduction
Schuster, H. G., & Just, W. (2005) · Wiley eBooks · doi:10.1002/3527604804 · Cited by 517
Generalized statistical complexity measures: Geometrical and analytical properties
Martin, M. T., Plastino, A., & Rosso, O. A. (2006) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2005.11.053 · Cited by 392
Self-citationIntensive entropic non-triviality measure
Lamberti, P. W., Martin, M. T., Plastino, A., & Rosso, O. A. (2004) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2003.11.005 · Cited by 342
Self-citationSimulation of Stationary Gaussian Processes in [0,1] d
Wood, A. T. A., & Chan, G. (1994) · Journal of Computational and Graphical Statistics · doi:10.2307/1390903 · Cited by 249
EEG analysis using wavelet-based information tools
Rosso, O. A., Martin, M. T., Figliola, A., Keller, K., & Plastino, A. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.10.009 · Cited by 205
Self-citation
Cited by
Horizontal visibility graphs: Exact results for random time series
Luque, B., Lacasa, L., Ballesteros, F., & Luque, J. (2009) · Physical Review E · doi:10.1103/physreve.80.046103 · Cited by 789
Forbidden patterns, permutation entropy and stock market inefficiency
Zunino, L., Zanin, M., Tabak, B. M., Pérez, D. G., & Rosso, O. A. (2009) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2009.03.042 · Cited by 249
Orlando, M., Dvorzhak, A., Bruentgens, F., Maglione, M., Rost, B. R., Sigrist, S. J., Breustedt, J., & Schmitz, D. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001149 · Cited by 30
Metadata
- Authors
- Rosso, O. A., Larrondo, H. A., Martin, M. T., Plastino, A., & Fuentes, M. A.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- 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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.