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Nonlinear Time Series Analysis
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  • Cited by 410
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Macau, Elbert E. N. 2019. A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems. Vol. 22, Issue. , p. 1.

    Hannisdal, Bjarte Liow, Lee Hsiang and Smith, Andrew 2018. Causality from palaeontological time series. Palaeontology, Vol. 61, Issue. 4, p. 495.

    Ogunsua, Babalola 2018. Low latitude ionospheric TEC responses to dynamical complexity quantifiers during transient events over Nigeria. Advances in Space Research, Vol. 61, Issue. 7, p. 1689.

    Širca, Simon and Horvat, Martin 2018. Computational Methods in Physics. p. 325.

    Blanco, Justin A. Johnson, Michael K. Jaquess, Kyle J. Oh, Hyuk Lo, Li-Chuan Gentili, Rodolphe J. and Hatfield, Bradley D. 2018. Quantifying Cognitive Workload in Simulated Flight Using Passive, Dry EEG Measurements. IEEE Transactions on Cognitive and Developmental Systems, Vol. 10, Issue. 2, p. 373.

    Sueur, Jérôme 2018. Sound Analysis and Synthesis with R. p. 247.

    Kashinath, Karthik Li, Larry K. B. and Juniper, Matthew P. 2018. Forced synchronization of periodic and aperiodic thermoacoustic oscillations: lock-in, bifurcations and open-loop control. Journal of Fluid Mechanics, Vol. 838, Issue. , p. 690.

    Yan, Bo Zhou, Shengxi and Litak, Grzegorz 2018. Nonlinear Analysis of the Tristable Energy Harvester with a Resonant Circuit for Performance Enhancement. International Journal of Bifurcation and Chaos, Vol. 28, Issue. 07, p. 1850092.

    Pavlova, Olga N. Pavlov, Alexey N. Derbov, Vladimir L. and Postnov, Dmitry E. 2018. Reconstruction of dynamical systems from resampled point processes produced by neuron models. p. 25.

    de Paula, Alexandre Vagtinski and Möller, Sergio Viçosa 2018. On the chaotic nature of bistable flows. Experimental Thermal and Fluid Science, Vol. 94, Issue. , p. 172.

    Ries, F. Nishad, K. Dressler, L. Janicka, J. and Sadiki, A. 2018. Evaluating large eddy simulation results based on error analysis. Theoretical and Computational Fluid Dynamics,

    Hattam, Laura and Greetham, Danica Vukadinović 2018. Energy Disaggregation for SMEs using Recurrence Quantification Analysis. p. 610.

    Konkoli, Zoran 2018. On developing theory of reservoir computing for sensing applications: the state weaving environment echo tracker (SWEET) algorithm. International Journal of Parallel, Emergent and Distributed Systems, Vol. 33, Issue. 2, p. 121.

    Lehnertz, Klaus Geier, Christian Rings, Thorsten and Stahn, Kirsten 2017. Capturing time-varying brain dynamics. EPJ Nonlinear Biomedical Physics, Vol. 5, Issue. , p. 2.

    McCamley, John Denton, William Lyden, Elizabeth and Yentes, Jennifer M. 2017. Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy and Cross Recurrence Quantification Analysis. Computational and Mathematical Methods in Medicine, Vol. 2017, Issue. , p. 1.

    Umeda, Yuhei 2017. Time Series Classification via Topological Data Analysis. Transactions of the Japanese Society for Artificial Intelligence, Vol. 32, Issue. 3, p. D-G72_1.

    Tabatabaei, Seyed Mostafa Dick, Scott and Xu, Wilsun 2017. Toward Non-Intrusive Load Monitoring via Multi-Label Classification. IEEE Transactions on Smart Grid, Vol. 8, Issue. 1, p. 26.

    Gorman, Jamie C. Dunbar, Terri A. Grimm, David and Gipson, Christina L. 2017. Understanding and Modeling Teams As Dynamical Systems. Frontiers in Psychology, Vol. 8, Issue. ,

    Oluwole, Olusegun S. A. 2017. Deterministic Chaos, El Niño Southern Oscillation, and Seasonal Influenza Epidemics. Frontiers in Environmental Science, Vol. 5, Issue. ,

    Løkse, Sigurd Bianchi, Filippo Maria and Jenssen, Robert 2017. Training Echo State Networks with Regularization Through Dimensionality Reduction. Cognitive Computation, Vol. 9, Issue. 3, p. 364.

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  • 2nd edition
  • Holger Kantz, Max-Planck-Institut für Physik komplexer Systeme, Dresden , Thomas Schreiber, Max-Planck-Institut für Physik komplexer Systeme, Dresden

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    Nonlinear Time Series Analysis
    • Online ISBN: 9780511755798
    • Book DOI: https://doi.org/10.1017/CBO9780511755798
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Book description

The paradigm of deterministic chaos has influenced thinking in many fields of science. Chaotic systems show rich and surprising mathematical structures. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic. The most direct link between chaos theory and the real world is the analysis of time series from real systems in terms of nonlinear dynamics. Experimental technique and data analysis have seen such dramatic progress that, by now, most fundamental properties of nonlinear dynamical systems have been observed in the laboratory. Great efforts are being made to exploit ideas from chaos theory wherever the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics, and many other sciences.

Reviews

From reviews of the first edition:‘… any serious physics institute should have such a book on its shelves. It will be of use to any experimental scientist dealing with nonlinear data or a theoretical physicist who desires a feeling of ‘how one does it in an experiment’. The clear course of presentation should make it accessible to undergraduate students.’

Daniel Wojcik Source: Pageoph

‘This book will be of value to any graduate student or researcher who needs to be able to analyse time series data, especially in the fields of physics, chemistry, biology, geophysics, medicine, economics and the social sciences.’

Source: Mathematical Reviews

'… a very readable introduction to the concepts and clear descriptions of the techniques, as well as cautions, where appropriate, about potential pitfalls and misuses of the methods. … the book is a good reference to the current state of the art from the nonlinear dynamics community and is important reading for anyone faced with interpreting irregular time series.'

Source: Contemporary Physics

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