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Some Aspects of Chaotic Time Series Analysis
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Some Aspects of Chaotic Time Series Analysis / Vladimir Golovko [et al.] // The 2nd International Conference on Neural Networks and Artificial Intelligence= Нейронные сети и искусственный интеллект : proceedings, Minsk, October 2–5, 2001 / Belarusian State University of Informatics and Radioelectronicsb ; ed.: Rauf Kh. Sadykhov [et al.]. – Minsk, 2001. – P. 66–69 : il. – Bibliogr.: p. 69 (5 titles).Abstract
We address two aspects in chaotic time series analysis, namely the definition of embedding parameters and the largest Lyapunov exponent. It is necessary for performing state space reconstruction and identification of chaotic behavior. For the first aspect, we examine the mutual information for determination of time delay and false nearest neighbors method for choosing appropriate
embedding dimension. For the second aspect we suggest neural network approach, which is characterized by simplicity and accuracy.
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