Automatic Autocorrelation and Spectral Ana...

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Beschreibung

"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® 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.

Über den Autor

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 autocorrelation analysis of stochastic data by the automatic selection of a suitable order and type for a time series model of the data.

Zusammenfassung

Spectral analysis requires subjective decisions which influence the final estimate and mean that different analysts can obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that is only acceptable if it is close to the best attainable accuracy for most types of stationary data. This book describes a method which fulfils the above near-optimal-solution criterion, taking advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data.

Inhaltsverzeichnis

Basic Concepts.- Periodogram and Lagged Product Autocorrelation.- 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.

Details

Erscheinungsjahr:	2010
Fachbereich:	Allgemeines
Genre:	Importe , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xii 298 S. 104 s/w Illustr. 298 p. 104 illus.
ISBN-13:	9781849965811
ISBN-10:	1849965811
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Broersen, Petrus M. T.
Auflage:	Softcover reprint of hardcover 1st edition 2006
Hersteller:	Springer Springer London Springer-Verlag London Ltd.
Verantwortliche Person für die EU:	Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße:	235 x 155 x 17 mm
Von/Mit:	Petrus M. T. Broersen
Erscheinungsdatum:	13.10.2010
Gewicht:	0,476 kg