A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is application/pdf
.
Online novelty detection on temporal sequences
2003
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03
Novelty detection, or anomaly detection, on temporal sequences has increasingly attracted attention from researchers in different areas. In this paper, we present a new framework for online novelty detection on temporal sequences. This framework includes a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm.
doi:10.1145/956750.956828
dblp:conf/kdd/MaP03
fatcat:dos7xoiwmrfkfh7sh5syzxr7iq