
SAS System for Forecasting Time Series
Wiley (Publisher)
2nd Edition
Published on 7. February 2006
Book
Paperback/Softback
424 pages
978-0-471-39566-9 (ISBN)
Description
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.
Reviews / Votes
"The new material and the update of the excellent 1E, now 17 years in the past, certainly make the 2E a necessary purchase for any user of SAS time series modeling methods."TechnometricsVol. 46, No. 1, February 2004More details
Product info
PB
Edition
2., Auflage
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Edition type
New edition
Product notice
Paperback (trade)
Unsewn / adhesive bound
Illustrations
Tables: 76 B&W, 0 Color; Graphs: 59 B&W, 0 Color
Dimensions
Height: 280 mm
Width: 210 mm
Thickness: 23 mm
Weight
1031 gr
ISBN-13
978-0-471-39566-9 (9780471395669)
Schweitzer Classification
Persons
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.
Content
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.