Precision and Recall for Time Series

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dc.contributor.authorTatbul, Nesime
dc.contributor.authorLee, Tae J
dc.contributor.authorZdonik, Stan
dc.contributor.authorAlam, Mejbah
dc.contributor.authorGottschlich, Justin E
dc.date2023-05-18T00:14:30.000
dc.date.accessioned2023-05-22T13:06:49Z
dc.date.available2023-05-22T13:06:49Z
dc.date.issued2018-01-01
dc.date.submitted2020-12-18T13:43:02-08:00
dc.description.abstractClassical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-based, meaning they occur over a period of time. Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences.
dc.identifier.urihttps://repository.upenn.edu/handle/20.500.14332/8495
dc.legacy.articleid1007
dc.legacy.fulltexturlhttps://repository.upenn.edu/cgi/viewcontent.cgi?article=1007&context=cps_machine_programming&unstamped=1
dc.source.issue9
dc.source.journalMachine Programming
dc.source.statuspublished
dc.titlePrecision and Recall for Time Series
dc.typePresentation
digcom.contributor.authorTatbul, Nesime
digcom.contributor.authorLee, Tae J
digcom.contributor.authorZdonik, Stan
digcom.contributor.authorAlam, Mejbah
digcom.contributor.authorisAuthorOfPublication|email:gojustin@cis.upenn.edu|institution:Intel|Gottschlich, Justin E
digcom.identifiercps_machine_programming/9
digcom.identifier.contextkey20689124
digcom.identifier.submissionpathcps_machine_programming/9
digcom.typeconference
dspace.entity.typePublication
relation.isAuthorOfPublication5cbcf403-a558-4c1c-aa8a-d700e3d50679
relation.isAuthorOfPublication5cbcf403-a558-4c1c-aa8a-d700e3d50679
relation.isAuthorOfPublication.latestForDiscovery5cbcf403-a558-4c1c-aa8a-d700e3d50679
upenn.schoolDepartmentCenterMachine Programming
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