



Retailers are commonly faced with the challenge of forecasting demand for items that are sold only at certain times of the year, but in high volumes. This paper suggests a compression approach for dealing with these kinds of highly seasonal forecasts using SAS software, which could produce more accurate results than forecasts based on standard time series modeling. It provides an illustrative example based on real-life data.
Data warehousing is expected to grow over the next 12 months despite the effects of the global economic crisis, IDC revealed.



