Online Change-point Detection Methods for Nonstationary Time Series

We present two new spectral-based methods for real-time detection of changes in autocorrelation structure in a continuous-valued time series. Our methods are built on the idea that changes in the autocorrelation 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 if a change has occurred. Through simulation, we evaluate the performance of our methods and find that they can provide reliable and quick 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.

By: Hyunyoung Choi; Hernando Ombao; Bonnie Ray

Published in: RC23520 in 2005


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