Overview

Applied Data Mining for Forecasting, 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.

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9781607646624

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Reviews

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 University I 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 Company Simply 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

Simply 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

I 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 Company 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 University Simply 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

Author Information

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 Forecast Server software. He received his MS 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 co-author 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 software, 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 MSc in Electrical Engineering from the Technical University of Varna, in Varna, Bulgaria, and a PhD in the same field, specializing in adaptive control systems, from the Technical University of Sofia, in Sofia, Bulgaria. He is a frequent presenter at computational intelligence conferences around the world.. Chip Wells has over 15 years of experience in implementing theoretical and applied econometrics using the SAS programming language and SAS Solutions. He was a Statistical Services Specialist in the SAS Education Division where he instructed and consulted with analysts from the Federal government and the financial, health care, oil, gas, and transportation industries. He is currently a Principal Analytic Consultant in the SAS Advanced Analytics Lab where he develops solutions that focus on time series analysis of financial variables and on building forecast models using sentiment data. Chip holds a PhD in Economics and an MA in Economics with a Statistics minor from North Carolina State University.

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