A novel distance correlation entropy and auto-distance correlation function for measuring the complexity of time series data

DOI10.1016/J.CNSNS.2024.108225MaRDI QIDQ6590974

Pengjian Shang, Yixiao Liu

Publication date: 21 August 2024

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

zbMATH Keywords

complexitytime series analysisdistance correlationauto-distance correlation function

Mathematics Subject Classification ID

Applications of statistics (62Pxx) Inference from stochastic processes (62Mxx) Multivariate analysis (62Hxx)

Cites Work

• Measuring and testing dependence by correlation of distances
• Detecting Novel Associations in Large Data Sets
• Complexity testing techniques for time series data: a comprehensive literature review
• Brownian distance covariance
• Determining Lyapunov exponents from a time series
• Complexity in economic and financial markets
• Measuring nonlinear dependence in time-series, a distance correlation approach
• Equitability, mutual information, and the maximal information coefficient
• Approximate entropy as a measure of system complexity.
• Feature Screening via Distance Correlation Learning
• Testing independence for multivariate time series via the auto-distance correlation matrix
• On the Calculation of Mutual Information
• Estimators of fractal dimension: assessing the roughness of time series and spatial data
• Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks
• A novel and effective method for quantifying complexity of nonlinear time series
• Dispersion complexity-entropy curves: an effective method to characterize the structures of nonlinear time series

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