Fr. 176.00

SAS for Forecasting Time Series

English · Paperback / Softback

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Informationen zum Autor John C. Brocklebank is Mgr. of Stats. Training at the SAS Institute. David A. Dickey is Associate Professor of Statistics at North Carolina State University. Klappentext Easy-to-read and comprehensive, this book shows how the SAS System performs multivariate time series analysis and features the advanced SAS procedures STATSPACE, ARIMA, and SPECTRA. The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analysed and the reults desired. Other topics covered include detecting sinusoidal components in time series models and performing bivariate corr-spectral analysis and comparing the results with the standard transfer function methodology. The authors? unique approach to integrating students in a variety of disciplines and industries. Emphasis is on correct interpretation of output to draw meaningful conclusions. The volume, co-pubished by SAS and JWS, features both theory and practicality, and accompanies a soon-to-be extensive library of SAS hands-on manuals in a multitude of statistical areas. The book can be used with a number of hardware-specific computing machines including CMS, Mac, MVS, Opem VMS Alpha, Opmen VMS VAX, OS/390, OS/2, UNIX, and Windows. Zusammenfassung Written in an easy-to-read style, this comprehensive book shows how the SAS System performs multivariate time series analysis, covering the advanced SAS procedures STATSPACE, ARIMA, and SPECTRA. Inhaltsverzeichnis Chapter 1- Overview of Time Series. Chapter 2- Simple Models: Autoregression. Chapter 3- The General ARIMA Model. Chapter 4- The ARIMA Model: Introductory Applications. Chapter 5- The ARIMA Model: Special Applications. Chapter 6- State Space Modeling. Chapter 7- Spectral Analysis.

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