Using scaling-region distributions to select embedding parameters
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Publication:2688095
DOI10.1016/j.physd.2023.133674OpenAlexW4318677125MaRDI QIDQ2688095
Elizabeth Bradley, Varad Deshmukh, Robert Meikle, Joshua Garland, James D. Meiss
Publication date: 9 March 2023
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2211.11511
Uses Software
Cites Work
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- Determining Lyapunov exponents from a time series
- Measuring the strangeness of strange attractors
- Embedology
- Practical method for determining the minimum embedding dimension of a scalar time series
- Automatic estimation of the correlation dimension for the analysis of electrocardiograms
- Central limit theorems for the Wasserstein distance between the empirical and the true distributions
- Most probable dimension value and most flat interval methods for automatic estimation of dimension from time series
- A practical method for calculating largest Lyapunov exponents from small data sets
- Practical implementation of nonlinear time series methods: The <scp>TISEAN</scp> package
- Independent coordinates for strange attractors from mutual information
- A unified approach to attractor reconstruction
- Calculation of the Wasserstein Distance Between Probability Distributions on the Line
- HOW MANY DELAY COORDINATES DO YOU NEED?
- Nonlinear time-series analysis revisited
- Deterministic Nonperiodic Flow
- Using curvature to select the time lag for delay reconstruction
- Differential Dynamical Systems, Revised Edition