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Technol."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data that is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this article proposes a novel\n            <jats:italic>Seasonal Ratio Scoring (SRS)<\/jats:italic>\n            approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods.\n          <\/jats:p>","DOI":"10.1145\/3630633","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T22:12:10Z","timestamp":1698703930000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Out-of-distribution Detection in Time-series Domain: A Novel Seasonal Ratio Scoring Approach"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8749-6632","authenticated-orcid":false,"given":"Taha","family":"Belkhouja","sequence":"first","affiliation":[{"name":"School of EECS, Washington State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9108-6767","authenticated-orcid":false,"given":"Yan","family":"Yan","sequence":"additional","affiliation":[{"name":"School of EECS, Washington State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3848-5301","authenticated-orcid":false,"given":"Janardhan Rao","family":"Doppa","sequence":"additional","affiliation":[{"name":"School of EECS, Washington State University, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.3012171"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.13543"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20552"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3224754"},{"key":"e_1_3_2_6_2","article-title":"A review on outlier\/anomaly detection in time series data","author":"Bl\u00e1zquez-Garc\u00eda Ane","year":"2021","unstructured":"Ane Bl\u00e1zquez-Garc\u00eda, Angel Conde, Usue Mori, and Jose A. 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