The Spectral Analysis of Time Series

Nonfiction, Science & Nature, Mathematics, Statistics, Business & Finance
Cover of the book The Spectral Analysis of Time Series by Lambert H. Koopmans, Elsevier Science
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Author: Lambert H. Koopmans ISBN: 9780080541563
Publisher: Elsevier Science Publication: May 18, 1995
Imprint: Academic Press Language: English
Author: Lambert H. Koopmans
ISBN: 9780080541563
Publisher: Elsevier Science
Publication: May 18, 1995
Imprint: Academic Press
Language: English

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.

  • 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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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