In this paper, we present a novel approach for local exceptionality detection on time series data. This method provides the ability to discover interpretable patterns in the data, which can be used to understand and predict the progression of a time series. This being an exploratory approach, the results can be used to generate hypotheses about the relationships between the variables describing a specific process and its dynamics. We detail our approach in a concrete instantiation and exemplary implementation, specifically in the field of teamwork research. Using a real-world dataset of team interactions we include results from an example data analytics application of our proposed approach, showcase novel analysis options, and discuss possible implications of the results from the perspective of teamwork research.

中文翻译：

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使用子组发现的时间序列局部异常检测

在本文中，我们提出了一种对时间序列数据进行局部异常检测的新方法。这种方法提供了发现数据中可解释模式的能力，可用于理解和预测时间序列的进展。这是一种探索性方法，结果可用于生成关于描述特定过程及其动态的变量之间关系的假设。我们在具体实例和示例性实现中详细说明了我们的方法，特别是在团队合作研究领域。使用团队互动的真实世界数据集，我们包括来自我们提出的方法的示例数据分析应用程序的结果，展示新颖的分析选项，并从团队合作研究的角度讨论结果的可能影响。