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- W4225366132 abstract "Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies." @default.
- W4225366132 created "2022-05-05" @default.
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- W4225366132 date "2022-01-08" @default.
- W4225366132 modified "2023-09-26" @default.
- W4225366132 title "Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis" @default.
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- W4225366132 doi "https://doi.org/10.1080/00273171.2021.1994848" @default.
- W4225366132 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35001769" @default.
- W4225366132 hasPublicationYear "2022" @default.
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