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CHAOTIC BEHAVIOR IN THE TIME SERIES OF POLLUTION CONCENTRATION
Abstract
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.
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