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.Giuseppe Leonardi obtained his MA degree in psychology from the University of Padua (Italy) and his PhD in experimental psychology at the University of Trieste (Italy). As a visiting student he was at the Center for Complex Systems at Florida Atlantic University (USA). His interests gradually focused on a dynamical approach in behavioral interactions and the methodological challenges this new approach requires. He especially concentrated on RQA and its applications to human language and cooperative behavior. From 2017 he has been at the University of Economics and Human Sciences in Warsaw (Poland), where he serves as dean of the School of Human Sciences since 2019.

This book is a solid introduction to recurrence-based analysis. It is very accessible. The authors explain the basic elements in clearlanguage that most methodologically-oriented students will find understandable. The book is also surprisingly comprehensive for such anintroductory treatment.

• Series Editor IntroductionAcknowledgmentsAbout the AuthorsAcronyms and NotationChapter 1: What is Recurrence Analysis?The Recurrence PlotDeriving Recurrence MeasuresAdvantages and Limitations of Recurrence AnalysisChapter 2: The Basics of Recurrence Analysis—Univariate RQAParameter EstimationThe Delay Parameter tThe Embedding Parameter mThe Radius Parameter eFurther ParametersSummarizing RQA OutputsChapter 3: The Bi-Variate Case: Cross-Recurrence Quantification AnalysisIntroduction to CRQAStandardizationAlignmentThe Cross-Recurrence Plot (CRP)Using CRQA With Continuous Data: Stock Market FluctuationsUsing CRQA With Categorical DataChapter 4: The Diagonal-Wise Cross-Recurrence Profile (DCRP)Diagonal-Wise Cross Recurrence Profiles (DCRP)Building a Baseline by Means of ShufflingChapter 5: Windowed Recurrence AnalysisIntroduction to Univariate Windowed Recurrence AnalysisWindowed Cross-Recurrence AnalysisUsing Windowed Recurrence Analysis for Continuous MonitoringChapter 6: Multivariate Analysis: Multidimensional Recurrence Quantification Analysis (MdRQA)Introduction to MdRQAMultidimensional Cross-Recurrence Quantification Analysis (MdCRQA)An Example Using Multidimensional RQA on Political Polling DataChapter 7: Sample Analysis and PracticalitiesCalculating General ParametersTime Series LengthComputing Confidence Bounds Via Boot-StrappingParameter ExplorationSurrogate AnalysisDealing With Multiple Recurrence-MeasuresChapter 8: ConclusionFurther ApplicationsFinding SoftwareA Final NoteReferencesIndex

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.Giuseppe Leonardi obtained his MA degree in psychology from the University of Padua (Italy) and his PhD in experimental psychology at the University of Trieste (Italy). As a visiting student he was at the Center for Complex Systems at Florida Atlantic University (USA). His interests gradually focused on a dynamical approach in behavioral interactions and the methodological challenges this new approach requires. He especially concentrated on RQA and its applications to human language and cooperative behavior. From 2017 he has been at the University of Economics and Human Sciences in Warsaw (Poland), where he serves as dean of the School of Human Sciences since 2019.

This book is a solid introduction to recurrence-based analysis. It is very accessible. The authors explain the basic elements in clearlanguage that most methodologically-oriented students will find understandable. The book is also surprisingly comprehensive for such anintroductory treatment.

• Series Editor IntroductionAcknowledgmentsAbout the AuthorsAcronyms and NotationChapter 1: What is Recurrence Analysis?The Recurrence PlotDeriving Recurrence MeasuresAdvantages and Limitations of Recurrence AnalysisChapter 2: The Basics of Recurrence Analysis—Univariate RQAParameter EstimationThe Delay Parameter tThe Embedding Parameter mThe Radius Parameter eFurther ParametersSummarizing RQA OutputsChapter 3: The Bi-Variate Case: Cross-Recurrence Quantification AnalysisIntroduction to CRQAStandardizationAlignmentThe Cross-Recurrence Plot (CRP)Using CRQA With Continuous Data: Stock Market FluctuationsUsing CRQA With Categorical DataChapter 4: The Diagonal-Wise Cross-Recurrence Profile (DCRP)Diagonal-Wise Cross Recurrence Profiles (DCRP)Building a Baseline by Means of ShufflingChapter 5: Windowed Recurrence AnalysisIntroduction to Univariate Windowed Recurrence AnalysisWindowed Cross-Recurrence AnalysisUsing Windowed Recurrence Analysis for Continuous MonitoringChapter 6: Multivariate Analysis: Multidimensional Recurrence Quantification Analysis (MdRQA)Introduction to MdRQAMultidimensional Cross-Recurrence Quantification Analysis (MdCRQA)An Example Using Multidimensional RQA on Political Polling DataChapter 7: Sample Analysis and PracticalitiesCalculating General ParametersTime Series LengthComputing Confidence Bounds Via Boot-StrappingParameter ExplorationSurrogate AnalysisDealing With Multiple Recurrence-MeasuresChapter 8: ConclusionFurther ApplicationsFinding SoftwareA Final NoteReferencesIndex

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.Giuseppe Leonardi obtained his MA degree in psychology from the University of Padua (Italy) and his PhD in experimental psychology at the University of Trieste (Italy). As a visiting student he was at the Center for Complex Systems at Florida Atlantic University (USA). His interests gradually focused on a dynamical approach in behavioral interactions and the methodological challenges this new approach requires. He especially concentrated on RQA and its applications to human language and cooperative behavior. From 2017 he has been at the University of Economics and Human Sciences in Warsaw (Poland), where he serves as dean of the School of Human Sciences since 2019.

This book is a solid introduction to recurrence-based analysis. It is very accessible. The authors explain the basic elements in clearlanguage that most methodologically-oriented students will find understandable. The book is also surprisingly comprehensive for such anintroductory treatment.

• Series Editor IntroductionAcknowledgmentsAbout the AuthorsAcronyms and NotationChapter 1: What is Recurrence Analysis?The Recurrence PlotDeriving Recurrence MeasuresAdvantages and Limitations of Recurrence AnalysisChapter 2: The Basics of Recurrence Analysis—Univariate RQAParameter EstimationThe Delay Parameter tThe Embedding Parameter mThe Radius Parameter eFurther ParametersSummarizing RQA OutputsChapter 3: The Bi-Variate Case: Cross-Recurrence Quantification AnalysisIntroduction to CRQAStandardizationAlignmentThe Cross-Recurrence Plot (CRP)Using CRQA With Continuous Data: Stock Market FluctuationsUsing CRQA With Categorical DataChapter 4: The Diagonal-Wise Cross-Recurrence Profile (DCRP)Diagonal-Wise Cross Recurrence Profiles (DCRP)Building a Baseline by Means of ShufflingChapter 5: Windowed Recurrence AnalysisIntroduction to Univariate Windowed Recurrence AnalysisWindowed Cross-Recurrence AnalysisUsing Windowed Recurrence Analysis for Continuous MonitoringChapter 6: Multivariate Analysis: Multidimensional Recurrence Quantification Analysis (MdRQA)Introduction to MdRQAMultidimensional Cross-Recurrence Quantification Analysis (MdCRQA)An Example Using Multidimensional RQA on Political Polling DataChapter 7: Sample Analysis and PracticalitiesCalculating General ParametersTime Series LengthComputing Confidence Bounds Via Boot-StrappingParameter ExplorationSurrogate AnalysisDealing With Multiple Recurrence-MeasuresChapter 8: ConclusionFurther ApplicationsFinding SoftwareA Final NoteReferencesIndex