The#Science#of#Anomaly#Detection!
! ! Page 3!
Anomaly!Detection!in!Streaming!Data"
Early anomaly detection in streaming data can be extremely valuable in many domains, such as IT
security, finance, vehicle tracking, health care, energy grid monitoring, e-commerce – essentially in
any application where there are sensors that produce important data changing over time. HTM-
based applications offer significant improvements over existing methods of anomaly detection for
streaming data:
Easy to use HTM-based methods don’t require training data or a separate training step. Automatic model
building and learning eliminates the need to manually define and maintain models and data
sets. This vastly reduces the time and effort for the user.
Scalable Unique models are automatically built for each metric being monitored.
Flexible Continual learning enables the application to continually update its model to recognize new or
changed behavior of each metric, without manual intervention.
Universal The same HTM learning algorithms work in many different domains where streaming data
analytics are important.
HTM-based applications excel at rapidly learning patterns and detecting abnormalities in streams of
metric data. Figure 1 below summarizes the key steps Numenta uses to model metrics and identify
anomalies. HTM for IT, Numenta’s HTM-based application for IT analytics, will be used as an example
in the following sections as we discuss these steps in further detail.
Figure 1 - Process for Modeling Metrics and Identifying Anomalies!
Building!Models!
HTM for IT is an HTM-based anomaly detection application for IT metrics. HTM for IT automatically
builds a separate model for each metric being monitored. Metric values are sent to HTM for IT, or
HTM for IT can request metric data. The metric value is combined with a time stamp, which helps
HTM for IT learn patterns related to time of day or day of week. The metric value and time are fed to
an encoder that converts them into a sparse distributed representation (SDR). SDRs enable several
useful attributes such as generalizability across data stream type, strong resistance to noise, and the
attachment of semantic meaning to data points.
SDR!
Metric!+!Time!
Encoder!
Prediction!!
Anomaly!Detection!
Classification!
Anom aly!
Score!
HTM!learning!
algorithms!
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