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ePub SAS for Forecasting Time Series download

by John C. Brocklebank,David A. Dickey

ePub SAS for Forecasting Time Series download
Author:
John C. Brocklebank,David A. Dickey
ISBN13:
978-0471395669
ISBN:
0471395668
Language:
Publisher:
WA (Wiley-SAS); 2 edition (July 14, 2003)
Category:
Subcategory:
Mathematics
ePub file:
1143 kb
Fb2 file:
1284 kb
Other formats:
lrf mbr docx doc
Rating:
4.9
Votes:
546

SAS performs univariate and multivariate time series analysis.

Книга SAS for Forecasting Time Series SAS for Forecasting Time Series Книги Математика Автор: John . P. Brocklebank, David A. Dickey Год издания: 2003 Формат: pdf Издат.

Chapter 6: Exponential Smoothing.

The time he spends exercising daily displays negative autocorrelation. Analysis Methods and SAS/ETS Software Modern statistical analysis is performed with software.

John C. Brocklebank, P.

Taking a tutorial approach. John C. Dr. Brocklebank received his P. in statistics and mathematics from North Carolina State University in 1981. He is often invited to conferences to speak about time series and statistical methods. Brocklebank, PhD, is Executive Vice President, Global Hosting and US Professional Services, at SAS. Brocklebank brings more than 35 years of SAS programming and statistical experience to his leadership role at SAS. He holds 14 patents and directs the SAS Advanced Analytics.

From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Dickey

John C. Dickey. 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.

SAS for Forecasting Time Series. Brocklebank; David A. Category: Математика.

Taking a tutorial approach, the authors focus on the procedures that most effectively bring results: the advanced procedures ARIMA, SPECTRA, STATESPACE, and VARMAX.

Read unlimited books and audiobooks on the web, iPad, iPhone and Android . To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject.

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.
  • It's a good book, but there's nothing there you couldn't get from much cheaper and more modern sources. Do people still use SAS, anyway?

  • Not user-friendly, even for analytics students:(

  • Fantastic!

  • The SAS Institute's Books by Users series contains many excellent manuals. The ones by Paul Allison (on survival analysis and on logistic regression) and by Stokes, Davis and Koch (categorical data analysis) are particularly well-written and illuminating. Unfortunately, Brocklebank and Dickey's contribution on time series analysis falls far short of the mean.

    The problem is not the statistical content, which is quite reliable, but rather than explanatory style. Chapters are disorganized, with many ideas introduced before being explained. Furthermore, the authors have adopted an unfortunate habit of constantly referring to "you" (i.e., the reader). "You" will do this. "You" will decide to do that. In many case, it was far from clear why such decisions would be made.

    The most serious problem, though, is the treatment of SAS code. This is supposed to be a book about ideas AND about syntax. But code is repeatedly presented with any kind of line-by-line explantion. Readers ("you" again) are left to wonder what the various elements of code mean, and how they control the computations done.

    I was very disappointed with this book. Unfortunately, the only alternative is to use the SAS documentation, and that's not really a very good alternative.

  • While the publishers describe SAS for Forecasting Time Series as a manual, the authors have provided more than SAS statements and the resulting outputs. Theoretical explanations, equations, and matrix algebra forms of equations fill the book. This superb manual is the product of the Research and Development Director of Analytic Solutions at SAS and of the Professor of Statistics who was the co-inventor of the Dickey-Fuller test. In addition to the coverage of the essential univariate and multivariate time series analysis topics (e.g., ARIMA models), the authors included entire chapters or large portions of chapters on: Cointegration, State Space Modeling, Spectral Analysis, and Data Mining.
    My only disappointment with this manual was the lack of an entire chapter on forecast accuracy. Four pages of references did not include a single reference to articles about forecasting competitions. The authors could have: (1) held back recent data in their examples (2) made forecasts with their best models (3) explained how to identify significant changes over time in error terms, standard errors, and in correlations (4) explained when and how to re-calculate model parameters (5) discussed the choice of unbiased forecast accuracy measures for comparing forecasts from ARIMA and regression models.

  • I had hoped that it would turn out to be at least a good book (if not excellent) by seeing such big names in the list of authors but am terribly disappointed! The table which normally could have been printed in a fraction of page has been printed on a complete page. Even if that was not enough, 5-6 pages continuously filled with 5-6 tables (all displaying the same meaning) can be commonly seen throughout the book. There is a terrible amount of repetition in printed matter also.
    It seems (though might be unintentionally) that a lot of stress has been given to enhance the page count of the book while giving almost no consideration to the quality of the material.
    In addition, a lot of important matter (for example non-linear time series models) have just not been covered.

  • .. the benchmark being SAS/ETS documentation. Let me recommend printing out selected chapters of SAS/ETS User's Guide - available online - describing the procedures that you (may) need, such as MODEL, FORECAST, ARIMA, VARMAX, UTC and STATESPACE.

  • If you're interested in advanced methods of forecasting time series data using SAS then this is the book to have. It is loaded with examples and interpretation of output as well as a nice concise explanation of theory. Everything you would expect from such renowned authors.