Neural network method for determining embedding dimension of a time series

DOI10.1016/j.cnsns.2010.10.030zbMath1220.65015OpenAlexW2136271007MaRDI QIDQ551124

Julien Clinton Sprott, Arne Maus

Publication date: 13 July 2011

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

Full work available at URL: https://doi.org/10.1016/j.cnsns.2010.10.030

zbMATH Keywords

predictionentropychaosneural networktime seriesLyapunov exponentembedding dimensionattractor dimensionlag space

Mathematics Subject Classification ID

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Cites Work

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• Localized Lyapunov exponents and the prediction of predictability
• A two-dimensional mapping with a strange attractor
• Measuring the strangeness of strange attractors
• Finite correlation dimension for stochastic systems with power-law spectra
• Embedology
• Predictability in the large: an extension of the concept of Lyapunov exponent
• PREDICTION OF CHAOTIC TIME SERIES WITH NEURAL NETWORKS AND THE ISSUE OF DYNAMIC MODELING
• Mathematical Models in Biology

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