Nonlinear Time Series Analysis

The time variability of many natural and social phenomena is not well described by standard methods of data analysis. However, nonlinear time series analysis uses chaos theory and nonlinear dynamics to understand seemingly unpredictable behavior. The results are applied to real data from physics, biology, medicine, and engineering in this volume. Researchers from all experimental disciplines, including physics, the life sciences, and the economy, will find the work helpful in the analysis of real world systems. First Edition Hb (1997): 0-521-55144-7 First Edition Pb (1997): 0-521-65387-8

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.

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Nonlinear Time Series Analysis

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