Automatic Autocorrelation and Spectral Analysis

2006. Auflage 2006
ISBN: 978-1-84628-328-4
Verlag: Springer

Buch, Englisch, 298 Seiten, Format (B × H): 165 mm x 245 mm, Gewicht: 581 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Broersen, Petrus M T

Fachgebiete

• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines

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.

Über Autor(innen)

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.