# Local Exceptionality Detection in Time Series Using Subgroup Discovery

arXiv: https://arxiv.org/abs/2108.11751 · HTML: https://mlsanity.com/paper/2108.11751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Distributed and parallel time series feature extraction for industrial big data applications](https://mlsanity.com/paper/1610.07717/) (2016)
- [Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery](https://mlsanity.com/paper/1701.07696/) (2017)
- [Mining Periodic Patterns with a MDL Criterion](https://mlsanity.com/paper/1807.01706/) (2018)
- [Social Analytics of Team Interaction using Dynamic Complexity Heat Maps and Network Visualizations](https://mlsanity.com/paper/2009.04445/) (2020)

## Abstract (arXiv)

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.