The pairwise approximate spatiotemporal symmetry algorithm: A method for segmenting time series pairs

Methods that measure the association between two intensively measured time series are of interest to researchers studying the symmetry of behaviors during social interaction. Such methods have historically focused on aggregating the amount of symmetry across all measurement occasions. However, it is...

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Bibliographic Details
Published inPsychological methods Vol. 29; no. 3; p. 435
Main Authors Sjobeck, Gustav R, Boker, Steven M, Scheidt, Carl E, Tschacher, Wolfgang
Format Journal Article
LanguageEnglish
Published United States 01.06.2024
Subjects
Algorithms
Data Interpretation, Statistical
Humans
Psychotherapy - methods
Social Interaction
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Summary:Methods that measure the association between two intensively measured time series are of interest to researchers studying the symmetry of behaviors during social interaction. Such methods have historically focused on aggregating the amount of symmetry across all measurement occasions. However, it is rarely expected that symmetry is present at all measurement occasions. The current method, the pairwise approximate spatiotemporal symmetry (PASS) algorithm, is an approach that may be used to determine which measurement occasions in pairwise time series are indicative of symmetry and which are not. This process thus divides time series into symmetric and nonsymmetric segments. The PASS algorithm is demonstrated here on representative simulated data and naturalistic psychotherapy data. Results suggest that the PASS algorithm has the potential to extract meaningful symmetry segments from human signals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
ISSN:1939-1463
DOI:10.1037/met0000341