To tailor time series models to a particular physical problem and to follow the working of various techniques
for processing and analyzing data, one must understand the basic theory of spectral (frequency domain) analysis
of time series. This classic book provides an introduction to the techniques and theories of spectral analysis
of time series. In a discursive style, and with minimal dependence on mathematics, the book presents the geometric
structure of spectral analysis. This approach makes possible useful, intuitive interpretations of important time
series parameters and provides a unified framework for an otherwise scattered collection of seemingly isolated
results.
The books strength lies in its applicability to the needs of readers from many disciplines with varying backgrounds
in mathematics. It provides a solid foundation in spectral analysis for fields that include statistics, signal
process engineering, economics, geophysics, physics, and geology. Appendices provide details and proofs for those
who are advanced in math. Theories are followed by examples and applications over a wide range of topics such as
meteorology, seismology, and telecommunications.
Topics covered include Hilbert spaces; univariate models for spectral analysis; multivariate spectral models; sampling,
aliasing, and discrete-time models; real-time filtering; digital filters; linear filters; distribution theory;
sampling properties ofspectral estimates; and linear prediction.
Key Features
* Hilbert spaces
* univariate models for spectral analysis
* multivariate spectral models
* sampling, aliasing, and discrete-time models
* real-time filtering
* digital filters
* linear filters
* distribution theory
* sampling properties of spectral estimates
* linear prediction