Recurrence-Based Analyses - Wallot, Sebastian; Leonardi, Giuseppe
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This book introduces techniques developed in physics and physiology for characterizing and analyzing patterns in time series data to a broad audience of social scientists. The authors illustrate concepts and techniques with relevant social science examples at different temporal scales: biweekly polling data on federal elections in Germany; daily values of three stock market indices; daily cases of SarsCov-19 in four countries during the pandemic; and second-by-second vocalizations of mothers and infants interacting recorded by motion cameras.

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Produktbeschreibung
This book introduces techniques developed in physics and physiology for characterizing and analyzing patterns in time series data to a broad audience of social scientists. The authors illustrate concepts and techniques with relevant social science examples at different temporal scales: biweekly polling data on federal elections in Germany; daily values of three stock market indices; daily cases of SarsCov-19 in four countries during the pandemic; and second-by-second vocalizations of mothers and infants interacting recorded by motion cameras.
Autorenporträt
Sebastian Wallot obtained his diploma in psychology from the University of Trier (Germany) and his PhD in experimental psychology from the University of Cincinnati, OH (USA). After postdoctoral positions at the University of Aarhus (Denmark) and the Max Planck Institute for Empirical Aesthetics in Frankfurt at the Main (Germany), he is currently working as Professor for research methods in psychology at Leuphana University of Lüneburg (Germany). His research if focused on joint action and reading from a dynamic systems perspective. Moreover, he is developing new analysis tools for time series - particularly in the area of recurrence and fractal analysis.