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 in an accompanying website.


Human behavior is an evolving phenomenon, and the description of its evolution is therefore a pertinent concern. Why do behaviors change or stay the same? How does behavioral transformation work? Under what conditions does change occur? While social science research may or may not explicitly address these issues, they linger in the background of any study as a potential story about the underlying dynamics of the behavior observed. To deal with such dynamics, we need to inquire about the details of the timing of events, and its impact on the behavior we are interested in, as well as investigate natural fluctuations in behavior that occur irrespective of any input from the environment. Measures of seasonal changes in temperature and precipitation, for example, reflect ...

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