Applied Data Mining for Forecasting Using SAS

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SAS Institute, Jul 2, 2012 - Computers - 336 pages

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable.

This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs.

This book is part of the SAS Press program.


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Data Mining for Forecasting Work Process
Data Mining for Forecasting Infrastructure
Issues with Data Mining for Forecasting Application
Data Collection
Data Preparation
A Practitioners Guide of DMM Methods for Forecasting
Model Building ARMA Models
Model Building ARIMAX or Dynamic Regression Modes
Model Building Further Modeling Topics
Model Building Alternative Modeling Approaches
An Example of Data Mining for Forecasting
Appendix A
Appendix B

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About the author (2012)

Tim Rey is Director of Advanced Analytics at the Dow Chemical Company, where he sets strategy and manages resources to deliver Advanced Analytics to Dow for strategic gain. A SAS user since 1979 and a JMP user since 1986, he specializes in JMP, SAS Enterprise Guide, SAS/STAT, SAS/ETS, SAS Enterprise Miner, and SAS FS. He received his M.S. in Forestry Biometrics (Statistics) from Michigan State University. A co-chair of M2008 and F2010, he presented keynote addresses at PBLS 2007, M2007, and A2007 Europe. In addition, he is coauthor of several papers, has appeared on multiple panels, and has given numerous talks at SAS conferences and other events as well as universities.

Arthur Kordon is Advanced Analytics Leader at the Dow Chemical Company, where he delivers solutions based on advanced analytics to Dow businesses, improves existing methods, consults, and teaches different levels of advanced analytics classes. Well versed in JMP, SAS Enterprise Guide, SAS Forecast Server, and SAS Enterprise Miner, he is the author of Applying Computational Intelligence: How to Create Value (2009), as well as ten book chapters and more than seventy journal and conference papers. Kordon received an M.Sc. in electrical engineering from the Technical University of Varna, in Varna, Bulgaria, and a Ph.D. in the same field, specializing in adaptive control systems, from the Technical University of Sofia, in Sofia, Bulgaria. He is frequent presenter at computational intelligence conferences around the world.

Chip Wells is Statistical Services Specialist at SAS Institute, where he creates, revises, and teaches courses associated with data mining and forecasting using SAS tools. A SAS user since 1991, Chip specializes in SAS Forecast Server, SAS Enterpriser Miner, SAS Model Manager, SAS/ETS, and SAS/OR. He received his PhD in economics from North Carolina State University.

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