  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.
