Sequential Change-Point Detection Methods for Nonstationary Time Series
Authors:
- 1 February 2008
- journal article
- Published by Taylor & Francis in Technometrics
- Vol. 50 (1) , 40-52
Abstract
We present two new spectral-based methods for detection of changes in autocorrelation structure in a continuous-valued time series in an online process monitoring setting. Our methods are based on the idea that changes in the autocorrelation structure are reflected by changes in the Fourier or wavelet-based spectrum and can be detected by comparing estimated spectra of adjacent blocks of the series. To be effective for slowly changing spectral structure, the methods are extended to allow information from more than one past block to be used in determining whether a change has occurred, in such a way as to minimize computational burden. Through simulation, we evaluate the performance of our methods and find that they can provide reliable and timely detection of changes in covariance structure in an online monitoring framework. We illustrate the methods using electroencephalogram traces (brain waves) and run-time computer performance metrics.Keywords
This publication has 19 references indexed in Scilit:
- A Change-Point Model for a Shift in VarianceJournal of Quality Technology, 2005
- Discrimination and Classification of Nonstationary Time Series Using the SLEX ModelJournal of the American Statistical Association, 2004
- The Changepoint Model for Statistical Process ControlJournal of Quality Technology, 2003
- Dynamically managing the communication-parallelism trade-off in future clustered processorsACM SIGARCH Computer Architecture News, 2003
- Automatic Local Smoothing for Spectral Density EstimationScandinavian Journal of Statistics, 1998
- Gaussian limit fields for the integrated periodogramThe Annals of Applied Probability, 1996
- Use of Cumulative Sums of Squares for Retrospective Detection of Changes of VarianceJournal of the American Statistical Association, 1994
- Detection of Multiple Changes of Variance Using Posterior OddsJournal of Business & Economic Statistics, 1993
- Nonparametric Comparison of Cumulative PeriodogramsJournal of the Royal Statistical Society Series C: Applied Statistics, 1991
- TESTS FOR COMPARING TWO ESTIMATED SPECTRAL DENSITIESJournal of Time Series Analysis, 1986