Discrimination of locally stationary time series using wavelets

Authors:
Elizabeth A. Maharaj;Andrés M. Alonso
Affiliations:
Department of Econometrics and Business Statistics, Monash University, Caulfield Campus, 900 Dandenong Road, Caulfield East, Vic. 3145, Australia;Department of Statistics, Universidad Carlos III de Madrid, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

Time series are sometimes generated by processes that change suddenly from one stationary regime to another, with no intervening periods of transition of any significant duration. A good example of this is provided by seismic data, namely, waveforms of earthquakes and explosions. In order to classify an unknown event as either an earthquake or an explosion, statistical analysts might be helped by having at their disposal an automatic means of identifying, at any time, which pattern prevails. Several authors have proposed methods to tackle this problem by combining the techniques of spectral analysis with those of discriminant analysis. The goal is to develop a discriminant scheme for locally stationary time series such as earthquake and explosion waveforms, by combining the techniques of wavelet analysis with those of discriminant analysis.