{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T23:44:06Z","timestamp":1771890246815,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T00:00:00Z","timestamp":1710028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Science and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["RGPIN-2017-05335"],"award-info":[{"award-number":["RGPIN-2017-05335"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000038","name":"Natural Science and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["CRDPJ 543705"],"award-info":[{"award-number":["CRDPJ 543705"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000038","name":"Natural Science and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["EINC BSP ENB-2016-010"],"award-info":[{"award-number":["EINC BSP ENB-2016-010"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100024492","name":"Enbridge Inc.","doi-asserted-by":"crossref","award":["RGPIN-2017-05335"],"award-info":[{"award-number":["RGPIN-2017-05335"]}],"id":[{"id":"10.13039\/501100024492","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100024492","name":"Enbridge Inc.","doi-asserted-by":"crossref","award":["CRDPJ 543705"],"award-info":[{"award-number":["CRDPJ 543705"]}],"id":[{"id":"10.13039\/501100024492","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100024492","name":"Enbridge Inc.","doi-asserted-by":"crossref","award":["EINC BSP ENB-2016-010"],"award-info":[{"award-number":["EINC BSP ENB-2016-010"]}],"id":[{"id":"10.13039\/501100024492","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Algorithms"],"abstract":"<jats:p>Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow learning in a wide variety of machine learning problems, and deep anomaly detection is very effective for point anomalies. However, deep semi-supervised contextual anomaly detection (in which anomalies within a time series are rare and none at all occur in the algorithm\u2019s training data) is a more difficult problem. Hybrid anomaly detectors (a \u201cnormal model\u201d followed by a comparator) are one approach to these problems, but the separate loss functions for the two components can lead to inferior performance. We investigate a novel synthetic-example oversampling technique to harmonize the two components of a hybrid system, thus improving the anomaly detector\u2019s performance. We evaluate our algorithm on two distinct problems: identifying pipeline leaks and patient-ventilator asynchrony.<\/jats:p>","DOI":"10.3390\/a17030114","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T04:51:12Z","timestamp":1710132672000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series"],"prefix":"10.3390","volume":"17","author":[{"given":"MohammadHossein","family":"Reshadi","sequence":"first","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"given":"Wen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"given":"Wenjie","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"given":"Precious","family":"Omashor","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"given":"Albert","family":"Dinh","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"given":"Jun","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4225-2770","authenticated-orcid":false,"given":"Scott","family":"Dick","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4655-3183","authenticated-orcid":false,"given":"Yuntong","family":"She","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Alberta Edmonton, Edmonton, AB T6G 1H9, Canada"}]},{"given":"Michael","family":"Lipsett","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Alberta Edmonton, Edmonton, AB T6G 1H9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,10]]},"reference":[{"key":"ref_1","unstructured":"Pang, G., Shen, C., Cao, L., and Hengel, A.v.d. 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