Business forecasters can elevate the value they bring their company by using data to help decision makers exploit market opportunities, overcome risks and more.
Luckily, data mining processes, methods and technology oriented to transactional data (data not having a time series framework), have grown immensely in the last 25 years. There’s significant value in the interdisciplinary notion of data mining for forecasting when used to solve time series problems.
In this session, we'll explore:
- How to wring maximum value out of available time series data.
Methodologies for and examples of utilizing data mining techniques specifically oriented to data collected over time.
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, Rey specializes in JMP, SAS® Enterprise Guide®, SAS/STAT®, SAS/ETS®, SAS® Enterprise MinerTM, and SAS® Forecast Server. He received his master's in forestry biometrics (statistics) from Michigan State University. A co-chair of M2008 and F2010, he presented keynote addresses at The Premier Business Leadership Series in 2007 as well as at M2007 and A2007 Europe.
Rey 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. His new book, Applied Data Mining for Forecasting, will be released in Fall 2012.