{"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/real-time-anomaly-detection-for-streaming","title":"Real-Time Anomaly Detection for Streaming Analytics","arxiv_id":"1607.02480","date":"2016-07-08","proceeding":null,"authors":["Subutai Ahmad","Scott Purdy"],"abstract":"Much of the worlds data is streaming, time-series data, where anomalies give\nsignificant information in critical situations. Yet detecting anomalies in\nstreaming data is a difficult task, requiring detectors to process data in\nreal-time, and learn while simultaneously making predictions. We present a\nnovel anomaly detection technique based on an on-line sequence memory algorithm\ncalled Hierarchical Temporal Memory (HTM). We show results from a live\napplication that detects anomalies in financial metrics in real-time. We also\ntest the algorithm on NAB, a published benchmark for real-time anomaly\ndetection, where our algorithm achieves best-in-class results.","url_abs":"http://arxiv.org/abs/1607.02480v1","url_pdf":"http://arxiv.org/pdf/1607.02480v1.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":"real-time-anomaly-detection-for-streaming","repo_url":"https://github.com/numenta/NAB","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"real-time-anomaly-detection-for-streaming","repo_url":"https://github.com/SudeepSarkar/matlabHTM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"real-time-anomaly-detection-for-streaming","repo_url":"https://github.com/ilialexander/htmau","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"real-time-anomaly-detection-for-streaming","repo_url":"https://github.com/ilialexander/rsm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-numenta-anomaly","task":"Anomaly Detection","dataset":"Numenta Anomaly Benchmark","model":"Bayesian Changepoint","rank_in_archive_order":8,"of":10,"metrics":{"NAB score":"17.7"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-numenta-anomaly","task":"Anomaly Detection","dataset":"Numenta Anomaly Benchmark","model":"Sliding Threshold","rank_in_archive_order":10,"of":10,"metrics":{"NAB score":"15.0"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}