JSM 2004 Online Program

Abstract #300321

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Activity Number:	232
Type:	Contributed
Date/Time:	Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor:	Section on Physical and Engineering Sciences
Abstract - #300321
Title:	Online Changepoint Methods for Time Series
Author(s):	Bonnie K. Ray*+ and Hyunyoung Choi and Hernando Ombao
Companies:	IBM T. J. Watson Research Center and University of Illinois, Urbana-Champaign and University of Illinois
Address:	Department of Mathematical Sciences, Yorktown Heights, NY, 10598,
Keywords:	spectral analysis ; changepoint detection
Abstract:	We present a new, spectral-based method for detecting changes in correlation structure in a continuous-valued time series. The test statistic can be efficiently computed and evaluated, making it appropriate for application in an online environment. Using simulation, we compare the proposed spectral-based method to existing spectral and parametric-based methods in terms of their performance in an online monitoring framework. We then present an application to monitoring of CPU metrics, for goals such as automated optimization of power consumption.

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