Automatic Autocorrelation and Spectral Analysis

By: Petrus M.T. Broersen

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Hardcover | 20 April 2006

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Hardcover
312 Pages

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23.39 x 15.6 x 1.91

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"Automatic Autocorrelation and Spectral Analysis" gives random data a language to communicate the information they contain objectively. It takes advantage of greater computing power and robust algorithms to produce enough candidate models of a given group of data to be sure of providing a suitable one. Improved order selection guarantees that one of the best (often the best) will be selected automatically. Written for graduate signal processing students and for researchers and engineers using time series analysis for 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 autocorrelation function of stochastic data can be estimated and interpreted in time series models;

- extensive support for the MATLAB (R) 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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Introduction	p. 1
Basic concepts	p. 11
Periodogram and lagged product autocorrelation	p. 29
ARMA theory	p. 59
Relations for time series models	p. 89
Estimation of time series models	p. 117
AR order selection	p. 167
MA and ARMA order selection	p. 209
ARMASA toolbox with applications	p. 223
Advanced topics in time series estimation	p. 251
Table of Contents provided by Blackwell. All Rights Reserved.

Automatic Autocorrelation and Spectral Analysis

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Hardcover

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