Detecting Abrupt Changes in a Piecewise Locally Stationary Time Series.
J Multivar Anal
; 99(2): 191-214, 2008 Feb.
Article
em En
| MEDLINE
| ID: mdl-19190715
ABSTRACT
Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Multivar Anal
Ano de publicação:
2008
Tipo de documento:
Article