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2021 | OriginalPaper | Buchkapitel

Generalized Symbolic Dynamics Approach for Characterization of Time Series

verfasst von : S. Suriyaprabhaa, Greeshma Gopinath, R. Sangeerthana, S. Alfiya, P. Asha, K. Satheesh Kumar

Erschienen in: Advances in Computing and Network Communications

Verlag: Springer Singapore

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Abstract

Various nonlinear methods have been developed to analyze the underlying dynamics of a nonlinear time series. Dynamic characterization using symbolic dynamics approach has been found to be a good alternative for the analysis of chaotic time series. As per this method, the given time series is first transformed into a single bit binary series. The single bit encoding limits its ability to capture the dynamics faithfully. This paper aims to provide a generalization of the symbolic dynamics method for better capturing the dynamical characteristics such as Lyapunov exponents of a time series. The effectiveness of the generalized method is demonstrated by employing a logistic map. The results of the analysis indicate that higher-order encoding can capture the bifurcation diagram more effectively compared to the original single bit encoding used in symbolic dynamics.

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Titel: Generalized Symbolic Dynamics Approach for Characterization of Time Series
verfasst von: S. Suriyaprabhaa
Greeshma Gopinath
R. Sangeerthana
S. Alfiya
P. Asha
K. Satheesh Kumar
Verlag: Springer Singapore
Buch: Advances in Computing and Network Communications
Print ISBN: 978-981-336-976-4

Electronic ISBN: 978-981-336-977-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-33-6977-1_5

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