SAS for Forecasting Time Series

Häftad, Engelska, 2003

1 463 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2003-07-14
Mått:211 x 279 x 23 mm
Vikt:989 g
Format:Häftad
Språk:Engelska
Antal sidor:424
Upplaga:2
Förlag:John Wiley & Sons Inc
ISBN:9780471395669

Utforska kategorier

Matematik inom Naturvetenskap och teknik

Mer om författaren

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.

Recensioner i media

"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 2004

Innehållsförteckning

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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