Local Exceptionality Detection in Time Series Using Subgroup Discovery

TL;DR

This paper introduces a new method for detecting local anomalies in time series data through subgroup discovery, enabling interpretable pattern recognition and hypothesis generation, demonstrated on teamwork interaction data.

Contribution

The paper presents a novel subgroup discovery approach for local exceptionality detection in time series, with a concrete implementation and real-world teamwork dataset application.

Findings

Identified interpretable patterns in team interaction data

Enabled hypothesis generation about team dynamics

Showcased new analysis options for time series data

Abstract

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.