Get 40% off of select print and eBooks in Engineering & Materials Science!

Automatic Autocorrelation and Spectral Analysis

Authors: Broersen, Petrus M.T.

Free Preview

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-1-84628-329-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $119.99
price for USA in USD
  • ISBN 978-1-84628-328-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $109.99
price for USA in USD
  • ISBN 978-1-84996-581-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this Textbook

Automatic Autocorrelation 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 stochastic 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 Autocorrelation 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 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.

About the authors

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.

Table of contents (10 chapters)

Table of contents (10 chapters)
  • Introduction

    Pages 1-9

  • Basic Concepts

    Pages 11-27

  • Periodogram and Lagged Product Autocorrelation

    Pages 29-57

  • ARMA Theory

    Pages 59-87

  • Relations for Time Series Models

    Pages 89-115

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-1-84628-329-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $119.99
price for USA in USD
  • ISBN 978-1-84628-328-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $109.99
price for USA in USD
  • ISBN 978-1-84996-581-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Automatic Autocorrelation and Spectral Analysis
Authors
Copyright
2006
Publisher
Springer-Verlag London
Copyright Holder
Springer-Verlag London
eBook ISBN
978-1-84628-329-1
DOI
10.1007/1-84628-329-9
Hardcover ISBN
978-1-84628-328-4
Softcover ISBN
978-1-84996-581-1
Edition Number
1
Number of Pages
XII, 298
Number of Illustrations
104 b/w illustrations
Topics