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2021 | OriginalPaper | Chapter

Generalized Symbolic Dynamics Approach for Characterization of Time Series

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

Published in: Advances in Computing and Network Communications

Publisher: 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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Metadata
Title
Generalized Symbolic Dynamics Approach for Characterization of Time Series
Authors
S. Suriyaprabhaa
Greeshma Gopinath
R. Sangeerthana
S. Alfiya
P. Asha
K. Satheesh Kumar
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Computing and Network Communications

Print ISBN: 978-981-336-976-4

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

Copyright Year: 2021

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