Across every industry, we are seeing an exponential increase in the availability of streaming, time-series data. The real-time detection of anomalies has significant practical application. Finding anomalies in such data can be very difficult, given the need to process data in real time, and learn while simultaneously making predictions. With the increasing variety of streaming data sources, automated deployment—without manual parameter tuning—is also becoming important.
Numenta’s online sequence memory algorithm, called Hierarchical Temporal Memory (HTM), has been used to detect anomalies in IT monitoring, human behavior, the stock market, geospatial data, and more. This webinar will introduce this novel technique, demonstrate its broad applicability, and cover performance details from a published benchmark designed for real-time anomaly detection.