Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection

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dc.contributor.authorGottschlich, Justin E
dc.contributor.authorGottschlich, Justin E
dc.contributor.authorTatbul, Nesime
dc.contributor.authorMetcalf, Eric
dc.contributor.authorZdonik, Stan
dc.date2023-05-18T00:14:33.000
dc.date.accessioned2023-05-22T13:06:44Z
dc.date.available2023-05-22T13:06:44Z
dc.date.issued2018-01-01
dc.date.submitted2020-12-18T14:04:07-08:00
dc.description.abstractThis short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.
dc.identifier.urihttps://repository.upenn.edu/handle/20.500.14332/8485
dc.legacy.articleid1009
dc.legacy.fulltexturlhttps://repository.upenn.edu/cgi/viewcontent.cgi?article=1009&context=cps_machine_programming&unstamped=1
dc.source.issue10
dc.source.journalMachine Programming
dc.source.statuspublished
dc.titleGreenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
dc.typePresentation
digcom.contributor.authorLee, Tae J
digcom.contributor.authorisAuthorOfPublication|email:gojustin@cis.upenn.edu|institution:Intel|Gottschlich, Justin E
digcom.contributor.authorTatbul, Nesime
digcom.contributor.authorMetcalf, Eric
digcom.contributor.authorZdonik, Stan
digcom.identifiercps_machine_programming/10
digcom.identifier.contextkey20689296
digcom.identifier.submissionpathcps_machine_programming/10
digcom.typeconference
dspace.entity.typePublication
relation.isAuthorOfPublication5cbcf403-a558-4c1c-aa8a-d700e3d50679
relation.isAuthorOfPublication.latestForDiscovery5cbcf403-a558-4c1c-aa8a-d700e3d50679
upenn.schoolDepartmentCenterMachine Programming
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