Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

TL;DR

The paper introduces the Numenta Anomaly Benchmark (NAB), a standardized, open-source framework for evaluating real-time anomaly detection algorithms on streaming, real-world time-series data across various domains.

Contribution

It presents NAB as a novel benchmark with a scoring algorithm tailored for real-time anomaly detection, enabling consistent comparison of different algorithms.

Findings

NAB provides a controlled environment for testing anomaly detectors.

Several open-source algorithms were evaluated using NAB.

Results highlight the strengths and weaknesses of different detection methods.

Abstract

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.