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

Fisch, Alexander T. M. and Eckley, Idris A. and Fearnhead, Paul (2022) A linear time method for the detection of collective and point anomalies. Statistical Analysis and Data Mining, 15 (4). pp. 494-508. ISSN 1932-1864

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Abstract

Abstract: The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Although 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, particularly in those settings where point anomalies might also occur. In this article, we introduce collective and point anomalies (CAPA), a computationally efficient approach that is suitable when collective anomalies are characterized by either a change in mean, variance, or both, and distinguishes them from point anomalies. 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 and its capacity to detect machine faults from temperature data.

Item Type:
Journal Article
Journal or Publication Title:
Statistical Analysis and Data Mining
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
171343
Deposited By:
Deposited On:
06 Jun 2022 10:15
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Jan 2023 11:35