Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
This addresses the problem for researchers and practitioners in domains like finance and IT by providing a standard framework for comparing anomaly detection algorithms, though it is incremental as it builds on existing methods by introducing a benchmark.
The paper tackles the lack of benchmarks for real-time anomaly detection in streaming time-series data by proposing the Numenta Anomaly Benchmark (NAB), which provides a controlled environment and scoring algorithm, and it evaluates several open-source algorithms on real-world datasets.
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.