2015/10/31 by Alexander Lavin, Subutai Ahmad · 5 citations
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #Data-Driven Disease Surveillance #Network Security and Intrusion Detection #cs.AI #cs.LG
paper · pdf · doi:10.1109/icmla.2015.141
14th International Conference on Machine Learning and Applications (IEEE ICMLA), 2015. Fixed typo in equation and formatting
arxiv created 2015/11/17 · openalex publication_date 2015/12/01 · openalex created_date 2016/06/24 · arxiv updated 2016/11/17 · openalex updated_date 2026/08/05
Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations; examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.