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Using Time Series to Analyze Long-Range Fractal Patterns
First Edition
- Matthijs Koopmans - Mercy College, USA
Volume:
185
Courses:
Intermediate/Advanced Statistics | Quantitative Methods | Quantitative Research Methods in Education | Quantitative Research Methods in Education | Statistics - General Interest | Statistics in Political Science | Statistics in Political Science | Statistics in Psychology | Statistics in Sociology |
Intermediate/Advanced Statistics | Quantitative Methods | Quantitative Research Methods in Education | Quantitative Research Methods in Education | Statistics - General Interest | Statistics in Political Science | Statistics in Political Science | Statistics in Psychology | Statistics in Sociology |
October 2020 | 120 pages | SAGE Publications, Inc
Using Time Series to Analyze Long Range Fractal Patterns 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. Author Matthijs Koopmans concentrates on two general approaches to irregularity in long time series: autoregressive fractionally integrated moving average models, and power spectral density analysis. He demonstrates the methods through two kinds of examples: simulations that illustrate the patterns that might be encountered and serve as a benchmark for interpreting patterns in real data; and secondly social science examples such a long range data on monthly unemployment figures, daily school attendance rates; daily numbers of births to teens, and weekly survey data on political orientation. Data and R-scripts to replicate the analyses are available on an accompanying website at https://study.sagepub.com/researchmethods/qass/koopmans-using-time-series.
Series Editor Introduction
Acknowledgments
About the Author
Chapter 1: Introduction
Chapter 2: Autoregressive Fractionally Integrated Moving Average or Fractional Differencing
Chapter 3: Power Spectral Density Analysis
Chapter 4: Related Methods in the Time and Frequency Domains
Chapter 5: Variations on the Fractality Theme
Chapter 6: Conclusion
References
Appendix
Index
Supplements
This is coherent treatment of fractal time-series methods that will be exceptionally useful.
Emory University
Each analysis is explained, and also the differences between the analyses are explained in a systematic way.
State University of New York, Plattsburgh
This volume offers a nice introduction to the various methods that can be used to discuss long range dependencies in univariate time series data. Koopmans makes a compelling case for these methods and offers clear exposition
University of Kansas
This amazing book provides a concise and solid foundation to the study of long-range process. In a short volume, the author successfully summarizes the theory of fractal approaches and provides many interesting and convincing examples. I highly recommend this book.
Rutgers University-Camden