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The expectations are to produce outputs characterized by a level of confidence. Thus, implementations need the Prediction Interval (PI) that quantify the level of uncertainty associated with the DNN point forecasts, which helps in making better-informed decision and mitigates against false anomaly alerts. An effort has been made in reducing false anomaly alerts through the use of quantile regression for identification of anomalies, but it is limited to the use of quantile interval to identify uncertainties in the data. In this paper, an improve time-series anomaly detection method called deep quantile regression anomaly detection (DQR-AD) is proposed. The proposed method go further to used quantile interval (QI) as anomaly score and compare it with threshold to identify anomalous points in time-series data. The tests run of the proposed method on publicly available anomaly benchmark datasets demonstrate its effective performance over other methods that assumed Gaussian distribution on the prediction or reconstruction cost for detection of anomalies. This shows that our method is potentially less sensitive to data distribution than existing approaches.<\/jats:p>","DOI":"10.1007\/s42979-021-00866-4","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T06:03:29Z","timestamp":1632981809000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series"],"prefix":"10.1007","volume":"2","author":[{"given":"Ahmad Idris","family":"Tambuwal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Neagu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"issue":"4","key":"866_CR1","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1109\/TII.2014.2300753","volume":"10","author":"L Da Xu","year":"2014","unstructured":"Da Xu L, He W, Li S. 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