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

Understand

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.