SAS System for Forecasting Time Series

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.

Autorentext

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.

Inhalt

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.