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In this paper, we present a novel approach for local exceptionality detectionon time series data. This method provides the ability to discover interpretablepatterns in the data, which can be used to understand and predict theprogression of a time series. This being an exploratory approach, the resultscan be used to generate hypotheses about the relationships between thevariables describing a specific process and its dynamics. We detail ourapproach in a concrete instantiation and exemplary implementation, specificallyin the field of teamwork research. Using a real-world dataset of teaminteractions we include results from an example data analytics application ofour proposed approach, showcase novel analysis options, and discuss possibleimplications of the results from the perspective of teamwork research.

Local Exceptionality Detection in Time Series Using Subgroup Discovery