Nonlinear Time Series Analysis

Produktbeschreibung

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. 

Inhaltsverzeichnis

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. 

Kritik

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 

Autoreninfo

Thomas Schreiber, Max-Planck-Institut für Physik komplexer Systeme, Dresden