A new criterion to distinguish stochastic and deterministic time series with the Poincaré section and fractal dimension

In this paper, we propose a new method for detecting regular behavior of time series: this method is based on the Poincaré section and the Higuchi fractal dimension. The new method aims to distinguish random signals from deterministic signals. In fact, our method provides a pattern for decision maki...

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Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2009-03, Vol.19 (1), p.013137-013137
Main Authors: Golestani, Abbas, Jahed Motlagh, M. R., Ahmadian, K., Omidvarnia, Amir H., Mozayani, Nasser
Format: Article
Language:English
Subjects:
Algorithms
Animals
Biophysics - methods
Fractals
Humans
Iris - anatomy & histology
Iris - physiology
Models, Statistical
Nonlinear Dynamics
Normal Distribution
Signal Processing, Computer-Assisted
Stochastic Processes
Time Factors
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Summary:In this paper, we propose a new method for detecting regular behavior of time series: this method is based on the Poincaré section and the Higuchi fractal dimension. The new method aims to distinguish random signals from deterministic signals. In fact, our method provides a pattern for decision making about whether a signal is random or deterministic. We apply this method to different time series, such as chaotic signals, random signals, and periodic signals. We apply this method to examples from all types of route to chaotic signals. This method has also been applied to data about iris tissues. The results show that the new method can distinguish different types of signals.
ISSN:1054-1500
1089-7682
DOI:10.1063/1.3096413