Targeted Event Detection
Abstract
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.
Cite
@article{arxiv.1003.2823,
title = {Targeted Event Detection},
author = {Werner Stuetzle and Donald B. Percival and Caren Marzban},
journal= {arXiv preprint arXiv:1003.2823},
year = {2010}
}
Comments
13 pages, 8 figures