Subutai Ahmad, Scott Purdy
Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in real-time. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Anomaly Detection | Numenta Anomaly Benchmark | NAB score | 17.7 | Bayesian Changepoint |
| Anomaly Detection | Numenta Anomaly Benchmark | NAB score | 15 | Sliding Threshold |