A linear time method for the detection of point and collective anomalies

Fisch, Alexander Tristan Maximilian and Eckley, Idris Arthur and Fearnhead, Paul (2018) A linear time method for the detection of point and collective anomalies. arXiv.

[thumbnail of 1806.01947v1]
PDF (1806.01947v1)

Download (1MB)


The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Whilst there has been substantial work aimed at making statistical analyses robust to outliers, or point anomalies, there has been much less work on detecting anomalous segments, or collective anomalies. By bringing together ideas from changepoint detection and robust statistics, we introduce Collective And Point Anomalies (CAPA), a computationally efficient approach that is suitable when collective anomalies are characterised by either a change in mean, variance, or both, and distinguishes them from point anomalies. Theoretical results establish the consistency of CAPA at detecting collective anomalies and empirical results show that CAPA has close to linear computational cost as well as being more accurate at detecting and locating collective anomalies than other approaches. We demonstrate the utility of CAPA through its ability to detect exoplanets from light curve data from the Kepler telescope.

Item Type:
Journal Article
Journal or Publication Title:
?? stat.mlcs.lgstat.apstat.me ??
ID Code:
Deposited By:
Deposited On:
27 Jul 2018 09:30
Last Modified:
13 May 2024 00:20