Chaotic behavior in the time series of pollution concentration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F18%3A50015023" target="_blank" >RIV/62690094:18470/18:50015023 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5593/sgem2018/4.2/S19.048" target="_blank" >http://dx.doi.org/10.5593/sgem2018/4.2/S19.048</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/sgem2018/4.2/S19.048" target="_blank" >10.5593/sgem2018/4.2/S19.048</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Chaotic behavior in the time series of pollution concentration

Popis výsledku v původním jazyce

The paper is focused on manifestations of chaotic behavior in the time series of pollution concentration. Deterministic chaos denotes a type of complex behavior of a deterministic dynamical system. The most important algorithms for data representation, prediction, phase space reconstruction, dimension, entropies, persistence and Lyapunov estimation are applicate here. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. Then we estimated the correlation dimension and Kolmogorov entropy. If the correlation dimension is low, the largest Lyapunov exponent is positive and the Kolmogorov entropy has a finite positive value, chaos is probably present. From these estimations it can be concluded that chosen time series are chaotic. The results indicated that chaotic behaviors obviously exist in this concentration time series.

Název v anglickém jazyce

Chaotic behavior in the time series of pollution concentration

Popis výsledku anglicky

The paper is focused on manifestations of chaotic behavior in the time series of pollution concentration. Deterministic chaos denotes a type of complex behavior of a deterministic dynamical system. The most important algorithms for data representation, prediction, phase space reconstruction, dimension, entropies, persistence and Lyapunov estimation are applicate here. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. Then we estimated the correlation dimension and Kolmogorov entropy. If the correlation dimension is low, the largest Lyapunov exponent is positive and the Kolmogorov entropy has a finite positive value, chaos is probably present. From these estimations it can be concluded that chosen time series are chaotic. The results indicated that chaotic behaviors obviously exist in this concentration time series.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Ecology, economics, education and legislation : conference proceedings

ISBN

978-619-7105-18-6

ISSN

1314-2704

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

365-372

Název nakladatele

SGEM

Místo vydání

Sofia

Místo konání akce

Albena

Datum konání akce

2. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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