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. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users-such as statisticians, economists, and data scientists-can now match the most sophisticated forecasting methods to the most current SAS applications.
Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. 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.
Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods:
Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition:
Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs.
This book is part of the SAS Press program.
Show moreTo 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. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users-such as statisticians, economists, and data scientists-can now match the most sophisticated forecasting methods to the most current SAS applications.
Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. 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.
Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods:
Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition:
Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs.
This book is part of the SAS Press program.
Show moreJohn C. Brocklebank, PhD, is Executive Vice President, Global Hosting and US Professional Services, at SAS. Dr. 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 Lab for State and Local Government, which devotes the resources of nearly 300 mostly doctoral-level SAS experts to devising technology solutions to critical state and local government issues. He also serves on the Board of Directors for the North Carolina State College of Sciences Foundation, where he advises the dean and college leaders on issues affecting the future direction of the college. In addition, he is a member of the Lipscomb University College of Computing and Technology Advancement Council and the Analytics Corporate Advisory Board, Analytics and Data Mining Programs, Spears School of Business at Oklahoma State University. Dr. Brocklebank holds an MS in biostatistics and in 1981 received a PhD in statistics and mathematics from North Carolina State University, where he now serves as a Physical and Mathematical Sciences Foundation board member and an adjunct professor of statistics.
"I highly recommend this book to anyone interested in using SAS to
fit time series models. I expect to consult the third edition even
more than I consulted the previous two editions." -- Professor
Simon Sheather "Interim Director of the Texas A&M Institute of
Data Science"
"The book, in many respects, offers an encyclopedic layout of time
series applications. Data for examples and the full SAS programing
is available on-line and is a valuable resource." -- Kenneth L.
Koonce, Professor Emeritus "Louisiana State University"
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