Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks
DOI10.1016/j.physa.2023.128670zbMath1518.62011OpenAlexW4328053287MaRDI QIDQ6045253
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Publication date: 26 May 2023
Published in: Physica A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physa.2023.128670
permutation entropyEEGordinal pattern transition networkspermutation weighted statistical transition entropytransition entropy
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Time series analysis of dynamical systems (37M10)
Cites Work
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- Permutation transition entropy: measuring the dynamical complexity of financial time series
- Fractional multiscale phase permutation entropy for quantifying the complexity of nonlinear time series
- Robustness and percolation of holes in complex networks
- Multifractal weighted permutation analysis based on Rényi entropy for financial time series
- Multiscale horizontal visibility entropy: measuring the temporal complexity of financial time series
- Financial time series analysis based on fractional and multiscale permutation entropy
- Characterizing dynamical transitions by statistical complexity measures based on ordinal pattern transition networks
- Complexity reduction for sign configurations through the KP II equation and its information-theoretic aspects
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