图书简介
This book presents methods for describing and analyzing dependency and irregularity in long time series. Irregularity refers to cycles that are similar in appearance, but unlike seasonal patterns more familiar to social scientists, repeated over a time scale that is not fixed. Until now, the application of these methods has mainly involved analysis of dynamical systems outside of the social sciences, but this volume makes it possible for social scientists to explore and document fractal patterns in dynamical social systems.
Series Editor Introduction \\ Acknowledgments \\ About the Author \\ Chapter 1: Introduction \\ A. Limitations of Traditional Approaches \\ B. Long-Range Dependencies \\ C. The Search for Complexity \\ D. Plan of the Book \\ Chapter 2: Autoregressive Fractionally Integrated Moving Average or Fractional Differencing \\ A. Basic Results in Time Series Analysis \\ B. Long-Range Dependencies \\ C. Application of the Models to Real Data \\ D. Chapter Summary and Reflection \\ Chapter 3: Power Spectral Density Analysis \\ A. From the Time Domain to the Frequency Domain \\ B. Spectral Density in Real Data \\ C. Fractional Estimates of Gaussian Noise and Brownian Motion \\ D. Chapter Summary and Reflection \\ Chapter 4: Related Methods in the Time and Frequency Domains \\ A. Estimating Fractal Variance \\ B. Spectral Regression \\ C. The Hurst Exponent Revisited \\ D. Chapter Summary and Reflection \\ Chapter 5: Variations on the Fractality Theme \\ A. Sensitive Dependence on Initial Conditions \\ B. The Multivariate Case \\ C. Regular Long-Range Processes and Nested Regularity \\ D. The Impact of Interventions \\ Chapter 6: Conclusion \\ A. Benefits and Drawbacks of Fractal Analysis \\ B. Interpretation of Parameters in Terms of Complexity Theory \\ C. A Note About the Software and Its Use \\ References \\ Appendix \\ Index
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