Machine Intelligence with Streaming Data.
A new approach for anomaly detection and time-based learning.
About the Webinar
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
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.