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Yet detecting anomalies in\nstreaming data is a difficult task, requiring detectors to process data in\nreal-time, not batches, and learn while simultaneously making predictions.\nThere are no benchmarks to adequately test and score the efficacy of real-time\nanomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which\nattempts to provide a controlled and repeatable environment of open-source\ntools to test and measure anomaly detection algorithms on streaming data. The\nperfect detector would detect all anomalies as soon as possible, trigger no\nfalse alarms, work with real-world time-series data across a variety of\ndomains, and automatically adapt to changing statistics. Rewarding these\ncharacteristics is formalized in NAB, using a scoring algorithm designed for\nstreaming data. NAB evaluates detectors on a benchmark dataset with labeled,\nreal-world time-series data. We present these components, and give results and\nanalyses for several open source, commercially-used algorithms. The goal for\nNAB is to provide a standard, open source framework with which the research\ncommunity can compare and evaluate different algorithms for detecting anomalies\nin streaming data.","url_abs":"http://arxiv.org/abs/1510.03336v4","url_pdf":"http://arxiv.org/pdf/1510.03336v4.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":"evaluating-real-time-anomaly-detection","repo_url":"https://github.com/numenta/NAB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"evaluating-real-time-anomaly-detection","repo_url":"https://github.com/askarmel/SDAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"evaluating-real-time-anomaly-detection","repo_url":"https://github.com/numenta/htmpapers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"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":[{"slug":"nab","name":"NAB","full_name":"Numenta Anomaly Benchmark"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-numenta-anomaly","task":"Anomaly Detection","dataset":"Numenta Anomaly Benchmark","model":"Numenta HTM","rank_in_archive_order":4,"of":10,"metrics":{"NAB score":"64.7"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-numenta-anomaly","task":"Anomaly Detection","dataset":"Numenta Anomaly Benchmark","model":"Twitter ADVec v1.0.0","rank_in_archive_order":6,"of":10,"metrics":{"NAB score":"47.1"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-numenta-anomaly","task":"Anomaly Detection","dataset":"Numenta Anomaly Benchmark","model":"Etsy Skyline","rank_in_archive_order":7,"of":10,"metrics":{"NAB score":"35.7"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-numenta-anomaly","task":"Anomaly Detection","dataset":"Numenta Anomaly Benchmark","model":"Random","rank_in_archive_order":9,"of":10,"metrics":{"NAB score":"16.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1510.03336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1510.03336"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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