  Ever growing volume and velocity of data coupled with decreasing attention
span of end users underscore the critical need for real-time analytics. In this
regard, anomaly detection plays a key role as an application as well as a means
to verify data fidelity. Although the subject of anomaly detection has been
researched for over 100 years in a multitude of disciplines such as, but not
limited to, astronomy, statistics, manufacturing, econometrics, marketing, most
of the existing techniques cannot be used as is on real-time data streams.
Further, the lack of characterization of performance -- both with respect to
real-timeliness and accuracy -- on production data sets makes model selection
very challenging. To this end, we present an in-depth analysis, geared towards
real-time streaming data, of anomaly detection techniques. Given the
requirements with respect to real-timeliness and accuracy, the analysis
presented in this paper should serve as a guide for selection of the "best"
anomaly detection technique. To the best of our knowledge, this is the first
characterization of anomaly detection techniques proposed in very diverse set
of fields, using production data sets corresponding to a wide set of
application domains.
