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More copies of this ISBN:Nonlinear Time Series Analysis 2ND Editionby Holger Kantz
Synopses & ReviewsPublisher Comments:Deterministic chaos provides a novel framework for the analysis of irregular time series. Traditionally, nonperiodic signals are modeled by linear stochastic processes. But even very simple chaotic dynamical systems can exhibit strongly irregular time evolution without random inputs. Chaos theory offers completely new concepts and algorithms for time series analysis which can lead to a thorough understanding of the signal. The book introduces a broad choice of such concepts and methods, including phase space embeddings, nonlinear prediction and noise reduction, Lyapunov exponents, dimensions and entropies, as well as statistical tests for nonlinearity. Related topics like chaos control, wavelet analysis and pattern dynamics are also discussed. Applications range from high quality, strictly deterministic laboratory data to short, noisy sequences which typically occur in medicine, biology, geophysics or the social sciences. All material is discussed and illustrated using real experimental data. Review:"...original, fundamentally honest, and very useful and valuable...an indispensable tool for [those] confronted with the analysis of possibly chaotic signals." Journal of Biological Sciences"The book is a good reference to the current state of the art from the nonlinear dynamics community and is importnant reading for anyone faced with interpreting irregular time series." Contemporary Physics, Professor R.S. MacKay Synopsis:The time variability of many natural and social phenomena is not well described by standard methods of data analysis. Nonlinear time series analysis uses chaos theory and nonlinear dynamics to understand such seemingly unpredictable behaviour. Results are applied to real data from physics, biology, medicine, and engineering. While based on a sound mathematical background, the book emphasises practical usefulness. Researchers from all experimental disciplines, including physics, the life sciences, and economy, will find guidance for the analysis of real world systems. Synopsis:First time paperback of very successful volume from Cambridge Nonlinear Science Series. Synopsis:The time variability of many natural and social phenomena is not well described by standard methods of data analysis. Nonlinear time series analysis uses chaos theory and nonlinear dynamics to understand such seemingly unpredictable behaviour. Results are applied to real data from physics, biology, medicine, and engineering. While based on a sound mathematical background, the book emphasises practical usefulness. Researchers from all experimental disciplines, including physics, the life sciences, and economy, will find guidance for the analysis of real world systems. Table of ContentsPart I. Basic Concepts: 1. Introduction: why nonlinear methods?; 2. Linear tools and general considerations; 3. Phase space methods; 4. Determinism and predictability; 5. Instability: Lyapunov exponents; 6. Self-similarity: dimensions; 7. Using nonlinear methods when determinism is weak; 8. Selected nonlinear phenomena; Part II. Advanced Topics: 9. Advanced embedding methods; 10. Chaotic data and noise; 11. More about invariant quantities; 12. Modeling and forecasting; 13. Chaos control; 14. Other selected topics; Appendix 1. Efficient neighbour searching; Appendix 2. Program listings; Appendix 3. Description of the experimental data sets.
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