Spectral Analysis of Non-Stationary Time Series
The last decade has seen a great popularity of application of the methods of the correlation and spectral analyses of time series [1–9]. The spectral approach to researches had a necessary condition: a given model of the series must be stationary. But in practice in most researches this condition is possible only within some limited time interval or impossible at all. At the same time a statistical stady of the spectra of these series is drowing more and more attention [4–12] despite the qualitativs and technical complexity of models. Spectral estimates obtained by time shift (they were systematically studied in [8,9]) suggested a new approach which is simple and convenient in most situations. Below is the study of a time series model which not only substantiates investigation of spectra changing in time but also brings us to the problems of the multivariable statistical analysis of these spectra: pattern recognition in the spectral domain, spectral desorders, spectral investigation when there are many strong non-stationary phenomena, and others. The size of the paper and some methodical considerations prevent demonstration of application of this approach to various problems ( its simplicity makes these problems quite easy and particularly logical from the researcher’s viewpoint.
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