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).

中文翻译：

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成对近似时空对称算法：一种分割时间序列对的方法。


研究社交互动过程中行为对称性的研究人员对测量两个集中测量的时间序列之间关联的方法感兴趣。历史上，此类方法侧重于聚合所有测量场合的对称量。然而，很少期望在所有测量场合都存在对称性。当前的方法，即成对近似时空对称（PASS）算法，是一种可用于确定成对时间序列中哪些测量时机指示对称性而哪些不指示对称性的方法。因此，这个过程将时间序列分为对称和非对称部分。此处在代表性模拟数据和自然心理治疗数据上演示了 PASS 算法。结果表明 PASS 算法有潜力从人类信号中提取有意义的对称片段。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。