Uniquely combining theory, application, and computing, this book explores the spectral approach to time series analysisThe use of periodically correlated (or cyclostationary) processes has become increasingly popular in a range of research areas such as meteorology, climate, communications, economics, and machine diagnostics. Periodically Correlated Random Sequences presents the main ideas of these processes through the use of basic definitions along with motivating, insightful, and illustrative examples. Extensive coverage of key concepts is provided, including second-order theory, Hilbert spaces, Fourier theory, and the spectral theory of harmonizable sequences. The authors also provide a paradigm for nonparametric time series analysis including tests for the presence of PC structures.Features of the book include:An emphasis on the link between the spectral theory of unitary operators and the correlation structure of PC sequencesA discussion of the issues relating to nonparametric time series analysis for PC sequences, including estimation of the mean, correlation, and spectrumA balanced blend of historical background with modern application-specific references to periodically correlated processesAn accompanying Web site that features additional exercises as well as data sets and programs written in MATLAB

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