{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-linear-time-method-for-the-detection-of","title":"A linear time method for the detection of point and collective anomalies","arxiv_id":"1806.01947","date":"2018-06-05","proceeding":null,"authors":["Alexander T. M. Fisch","Idris A. Eckley","Paul Fearnhead"],"abstract":"The challenge of efficiently identifying anomalies in data sequences is an\nimportant statistical problem that now arises in many applications. Whilst\nthere has been substantial work aimed at making statistical analyses robust to\noutliers, or point anomalies, there has been much less work on detecting\nanomalous segments, or collective anomalies, particularly in those settings\nwhere point anomalies might also occur. In this article, we introduce\nCollective And Point Anomalies (CAPA), a computationally efficient approach\nthat is suitable when collective anomalies are characterised by either a change\nin mean, variance, or both, and distinguishes them from point anomalies.\nTheoretical results establish the consistency of CAPA at detecting collective\nanomalies and, as a by-product, the consistency of a popular penalised cost\nbased change in mean and variance detection method. Empirical results show that\nCAPA has close to linear computational cost as well as being more accurate at\ndetecting and locating collective anomalies than other approaches. We\ndemonstrate the utility of CAPA through its ability to detect exoplanets from\nlight curve data from the Kepler telescope.","url_abs":"http://arxiv.org/abs/1806.01947v2","url_pdf":"http://arxiv.org/pdf/1806.01947v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-linear-time-method-for-the-detection-of","repo_url":"https://github.com/Fisch-Alex/anomaly","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}