{"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/long-short-term-memory-networks-for-anomaly","title":"Long Short Term Memory Networks for Anomaly Detection in Time Series","arxiv_id":null,"date":"2015-04-22","proceeding":"ESANN 2015 4","authors":["Pankaj Malhotra","Lovekesh Vig","Gautam Shroff","Puneet Agarwal"],"abstract":"Long Short Term Memory (LSTM) networks have been demonstrated to be particularly useful for learning sequences containing longer term patterns of unknown length, due to their ability to maintain long term memory. Stacking recurrent hidden layers in such networks also enables the learning of higher level temporal features, for faster learning with sparser representations. In this paper, we use stacked LSTM networks for anomaly/fault detection in time series. A network is trained on non-anomalous data and used as a predictor over a number of time steps. The resulting prediction errors are modeled as a multivariate Gaussian distribution, which is used to assess the likelihood of anomalous behavior. The efficacy of this approach is demonstrated on four datasets: ECG, space shuttle, power demand, and multi-sensor engine dataset.","url_abs":"https://www.esann.org/proceedings/2015#293","url_pdf":"https://www.esann.org/sites/default/files/proceedings/legacy/es2015-56.pdf","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":"long-short-term-memory-networks-for-anomaly","repo_url":"https://github.com/KDD-OpenSource/DeepADoTS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"fault-detection","task_name":"Fault Detection"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-anomaly-detection","task_name":"Time Series Anomaly Detection"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}