NAB (Numenta Anomaly Benchmark)

Introduced by Lavin et al. in Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

The First Temporal Benchmark Designed to Evaluate Real-time Anomaly Detectors Benchmark

The growth of the Internet of Things has created an abundance of streaming data. Finding anomalies in this data can provide valuable insights into opportunities or failures. Yet it’s difficult to achieve, due to the need to process data in real time, continuously learn and make predictions. How do we evaluate and compare various real-time anomaly detection techniques?

The Numenta Anomaly Benchmark (NAB) provides a standard, open source framework for evaluating real-time anomaly detection algorithms on streaming data. Through a controlled, repeatable environment of open-source tools, NAB rewards detectors that find anomalies as soon as possible, trigger no false alarms, and automatically adapt to any changing statistics.

NAB comprises two main components: a scoring system designed for streaming data and a dataset with labeled, real-world time-series data.

Source: Evaluating Real-time Anomaly Detection Algorithms – the Numenta Anomaly Benchmark

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