W. Stuetzle, D. B. Percival and C. Marzban (2010),
`Targeted Event Detection,'
submitted 1/27/10.
Summary
We consider the problem of event detection based upon a (typically
multivariate) data stream characterizing some system. Most of the
time the system is quiescent -- nothing of interest is happening --
but occasionally events of interest occur. The goal of event detection is
to raise an alarm as soon as possible after the onset of an
event. A simple way of addressing the event detection problem is to look
for changes in the data stream and equate ``change'' with ``onset of
event''. However, there might be many kinds of changes in the stream
that are uninteresting. We assume that we are given a segment of the
stream where interesting events have been marked. We propose a method
for using these training data to construct a ``targeted'' detector
that is specifically sensitive to changes signaling the onset of
interesting events.
Key Words
Change point detection;
Event detection;
Image analysis;
Surveillance;
Time series analysis
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