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Automatic Autocorrelation and Spectral Analysis / Edition 1

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Overview

Automatic Auorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively.

In the current practice of spectral analysis, subjective decisions have to be made all of which influence the final spectral estimate and mean that different analysts obtain different results from the same stationary shastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that solution is only acceptable if it is close to the best attainable accuracy for most types of stationary data.

Automatic Auorrelation and Spectral Analysis describes a method which fulfils the above near-optimal-solution criterion. It takes advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the best (and often the best) will be selected automatically. The data themselves suggest their best representation. Should the analyst wish to intervene, alternatives can be provided. Written for graduate signal processing students and for researchers and engineers using time series analysis for practical applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers:

• tuition in how power spectral density and the auorrelation function of shastic data can be estimated and interpreted in time series models;

• extensive support for the MATLAB® ARMAsel toolbox;

• applications showing the methods in action;

• appropriate mathematics for students to apply the methods with references for those who wish to develop them further.

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Product Details

  • ISBN-13: 9781849965811
  • Publisher: Springer London
  • Publication date: 10/13/2010
  • Edition description: Softcover reprint of hardcover 1st ed. 2006
  • Edition number: 1
  • Pages: 298
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.65 (d)

Meet the Author

Piet M.T. Broersen received the Ph.D. degree in 1976, from the Delft University of Technology in the Netherlands.

He is currently with the Department of Multi-scale Physics at TU Delft. His main research interest is in automatic identification on statistical grounds. He has developed a practical solution for the spectral and auorrelation analysis of shastic data by the automatic selection of a suitable order and type for a time series model of the data.

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Table of Contents

Introduction.- Basic Concepts.- Periodogram and Lagged Product Auorrelation.- ARMA Theory.- Relations for Time Series Models.- Estimation of Time Series Models.- AR Order Selection.- MA and ARMA Order Selection.- ARMASA Toolbox with Applications.- Advanced Topics in Time Series Estimation. ARMASA Toolbox.

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