Nonlinear Time Series Analysis by Holger KantzNonlinear Time Series Analysis by Holger Kantz

Nonlinear Time Series Analysis

byHolger Kantz, Thomas Schreiber

Paperback | January 26, 2004

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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
Title:Nonlinear Time Series AnalysisFormat:PaperbackDimensions:388 pages, 9.72 × 6.85 × 0.79 inPublished:January 26, 2004Publisher:Cambridge University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0521529026

ISBN - 13:9780521529020

Reviews

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.

Editorial Reviews

"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