{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepant-a-deep-learning-approach-for","title":"DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series","arxiv_id":null,"date":"2018-12-19","proceeding":null,"authors":["Mohsin Munir","Shoaib Ahmed Siddiqui","Andreas Dengel","Sheraz Ahmed"],"abstract":"Traditional distance and density-based anomaly detection techniques are unable to detect\r\nperiodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap\r\nin time series anomaly detection in the current era of the IoT. To address this problem, we present a novel deep\r\nlearning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to\r\nthe non-streaming cases. DeepAnT is capable of detecting a wide range of anomalies, i.e., point anomalies,\r\ncontextual anomalies, and discords in time series data. In contrast to the anomaly detection methods where\r\nanomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to\r\nforecast the normal behavior of a time series. DeepAnT consists of two modules: time series predictor and\r\nanomaly detector. The time series predictor module uses deep convolutional neural network (CNN) to predict\r\nthe next time stamp on the de\u001cned horizon. This module takes a window of time series (used as a context)\r\nand attempts to predict the next time stamp. The predicted value is then passed to the anomaly detector\r\nmodule, which is responsible for tagging the corresponding time stamp as normal or abnormal. DeepAnT\r\ncan be trained even without removing the anomalies from the given data set. Generally, in deep learning based\r\napproaches, a lot of data are required to train a model. Whereas in DeepAnT, a model can be trained\r\non relatively small data set while achieving good generalization capabilities due to the effective parameter\r\nsharing of the CNN. As the anomaly detection in DeepAnT is unsupervised, it does not rely on anomaly\r\nlabels at the time of model generation. Therefore, this approach can be directly applied to real-life scenarios\r\nwhere it is practically impossible to label a big stream of data coming from heterogeneous sensors comprising\r\nof both normal as well as anomalous points. We have performed a detailed evaluation of 15 algorithms on\r\n10 anomaly detection benchmarks, which contain a total of 433 real and synthetic time series. Experiments\r\nshow that DeepAnT outperforms the state-of-the-art anomaly detection methods in most of the cases, while\r\nperforming on par with others.","url_abs":"https://ieeexplore.ieee.org/document/8581424","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8581424","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepant-a-deep-learning-approach-for","repo_url":"https://github.com/EnsiyeTahaei/DeepAnT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deepant-a-deep-learning-approach-for","repo_url":"https://github.com/bmonikraj/medium-ds-unsupervised-anomaly-detection-deepant-lstmae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"deepant-a-deep-learning-approach-for","repo_url":"https://github.com/datacubeR/DeepAnt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deepant-a-deep-learning-approach-for","repo_url":"https://github.com/swlee23/deep-learning-time-series-anomaly-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-anomaly-detection","task_name":"Time Series Anomaly Detection"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}