PÁNIS, Radim, Martin KOLOŠ and Zdeněk STUCHLÍK. Detection of chaotic behavior in time series. In Proceedings of RAGtime 22: Workshops on black holes and neutron stars. Opava: Slezská univerzita v Opavě, Fyzikální ústav v Opavě, 2020, p. 221-231. ISBN 978-80-7510-432-8.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name Detection of chaotic behavior in time series
Authors PÁNIS, Radim (703 Slovakia, belonging to the institution), Martin KOLOŠ (203 Czech Republic, belonging to the institution) and Zdeněk STUCHLÍK (203 Czech Republic, belonging to the institution).
Edition Opava, Proceedings of RAGtime 22: Workshops on black holes and neutron stars, p. 221-231, 11 pp. 2020.
Publisher Slezská univerzita v Opavě, Fyzikální ústav v Opavě
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10308 Astronomy
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/47813059:19630/20:A0000053
Organization unit Institute of physics in Opava
ISBN 978-80-7510-432-8
ISSN 2336-5676
Keywords in English chaos; fractal dimension; recurrence quantification analysis; machine learning; logistic map; time series; tent map
Tags EF19_073-0016951, FÚ, FÚ2020, RAGtime 22, RIV21, SGF-4-2020, SGS12-2019
Tags International impact
Links EF19_073/0016951, research and development project.
Changed by Changed by: Mgr. Pavlína Jalůvková, učo 25213. Changed: 19/4/2021 18:11.
Abstract
Deterministic chaos is phenomenon from nonlinear dynamics and it belongs togreatest advances of twentieth-century science. Chaotic behavior appears apart ofmathematical equations also in wide range in observable nature, so as in there orig-inating time series. Chaos in time series resembles stochastic behavior, but apart ofrandomness it is totally deterministic and therefore chaotic data can provide us use-ful information. Therefore it is essential to have methods, which are able to detectchaos in time series, moreover to distinguish chaotic data from stochastic one. Herewe present and discuss the performance of standard and machine learning methodsfor chaos detection and its implementation on two well known simple chaotic dis-crete dynamical systems - Logistic map and Tent map, which fit to the most of thedefinitions of chaos.
PrintDisplayed: 6/5/2024 08:20