Applied Data Mining for Forecasting Using SAS
This well-organized and well-written book is unusual in that it takes you through the complete forecasting process-from the beginning planning stages through data collection, cleaning, and final analysis with a nice summary example in the last chapter that ties everything together. Discussion of the many decisions you'll need to make that were not covered in your statistics textbooks make this an especially useful reference for those who actually do forecasting. It covers a lot of ground from simple to quite sophisticated modeling techniques without excessive mathematical detail. --David Dickey, William Neal Reynolds Distinguished Professor of Statistics, North Carolina State UniversityI believe this is an excellent book for frontier practitioners and researchers, especially in the forecasting and data mining fields. Leveraging data mining techniques in an era where economic data and industry trend data are ample and readily available for building casual-effect forecasting models is a very promising endeavor. With a good causal-effect predictive model built through this approach, businesses would know what external and internal factors are truly driving effects and scenario-based forecasts and hence various contingency plans could be made in a timely fashion. I applaud the great effort by the authors. --Jerry Z. Shan, Principal Scientist, HP Labs, Hewlett-Packard CompanySimply put, Applied Data Mining for Forecasting Using SAS, written by Rey, Kordon, and Wells, adds much to the literature on the topic of forecasting. Its applied, data-driven focus makes this book amenable to practitioners. Additionally, this book should be adopted in the academic community, especially in graduate courses that focus on forecasting. --Oral Capps, Jr., Regents Professor, Executive Professor, and Co-Director, The Agribusiness, Food and Consumer Economics Research Center (AFCERC), Texas A&M; University