Automatic Autocorrelation and Spectral Analysis / Edition 1

by Petrus M.T. Broersen

Overview

"Automatic Auorrelation 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 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.

Product Details

ISBN-13:	9781849965811
Publisher:	Springer London
Publication date:	10/13/2010
Edition description:	Softcover reprint of hardcover 1st ed. 2006
Pages:	298
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

About 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.

Table of Contents

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.