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

Fisch, Alex and Eckley, Idris Arthur and Fearnhead, Paul Nicholas (2018) A linear time method for the detection of point and collective anomalies. Working Paper. Arxiv.

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Abstract

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:
Monograph (Working Paper)
ID Code:
144320
Deposited By:
Deposited On:
27 May 2020 15:05
Refereed?:
No
Published?:
Published
Last Modified:
04 Oct 2020 23:45