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Nonlinear Time Series Analysis
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Table of Contents

Preface; Acknowledgements; Part I. Basic Topics: 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. Modelling and forecasting; 13. Non-stationary signals; 14. Coupling and synchronisation of nonlinear systems; 15. Chaos control; Appendix A: using the TISEAN programs; Appendix B: description of the experimental data sets; References; Index.

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New edition of a successful advanced text on nonlinear time series analysis.

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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, 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.' 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.' Contemporary Physics

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