Preface; 1. The nature of time series and their frequency analysis; 2. Foundations; 3. Analytic properties of Fourier transforms and complex matrices; 4. Stochastic properties of finite Fourier transforms; 5. The estimation of power spectra; 6: Analysis of a linear time invariant relation between a stochastic series and several deterministic series; 7. Estimating the second-order spectra of vector-valued series; 8. Analysis of a linear time invariant relation between two vector-valued stochastic series; 9. Principal components in the frequency domain; 10. The canonical analysis of time series; Proofs of theorems; References; Notation index; Author index; Subject index; Addendum.
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Description
Intended for students and researchers, this text employs basic techniques of univariate and multivariate statistics for the analysis of time series and signals. It covers a broad collection of theorems. The techniques are illustrated by data analyses and are discussed both heuristically and formally to serve both the applied and the theoretical worker. IEEE Signal Processing Magazine