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Detecting hidden changes in the dynamics of noisy nonlinear time series by using the Jensen–Shannon divergence

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Abstract

A new algorithm is described for detecting hidden changes in the topological structure of the dynamics of a nonlinear system due to the perturbations in the driving signals. The proposed method is based on the use of a double sliding window and wavelet decomposition and calculates the Jensen–Shannon divergence between the probability distributions of the normalised wavelet coefficients of the first half of the sliding window and those of the second half of the sliding window. Applying the proposed approach to the Duffing and Hénon–Heiles systems, real-life signals showed their effectiveness at detecting small discontinuities in the dynamic behaviour of the system even when the system is chaotic.

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Authors and Affiliations

  1. Department of Health Promotion and Rehabilitation, Lithuanian Sports University, Sporto 6, 44221, Kaunas, Lithuania

    Kazimieras Pukenas

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  1. Kazimieras Pukenas
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Correspondence to Kazimieras Pukenas.

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Pukenas, K. Detecting hidden changes in the dynamics of noisy nonlinear time series by using the Jensen–Shannon divergence. Pramana - J Phys 97, 190 (2023). https://doi.org/10.1007/s12043-023-02668-0

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  • DOI: https://doi.org/10.1007/s12043-023-02668-0

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