Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/63424
Title: Data mining for optimal combination demand forecasts
Authors: Chan, CK 
Wong, H 
Pang, WK
Troutt, MD
Issue Date: 2003
Publisher: Idea Group Publishing
Source: In PC Pendharkar (Ed.), Managing data mining technologies in organizations : techniques and applications, p. 106-122. Hershey, PA: Idea Group Publishing, 2003 How to cite?
Abstract: This chapter is a case study in combining forecasts for inventory management in which the need for data mining in combination forecasts is necessary. The need comes from selection of sample items on which forecasting strategy can be made for all items, selection of constituent forecasts to be combined and selection of weighting method for the combination. A leading bank in HongKong consumes more than 300 kinds of printed forms for its daily operations. A major problem of its inventory control system for such forms management is to forecast their monthly demand. The bank currently uses simple forecasting methods such as simple moving average and simple exponential smoothing for its inventory demands. In this research, the individual forecasts come from wellestablished time series models. The weights for combination are estimated with quadratic programming. The combined forecast is found to perform better than any of the individual forecasts. Some insights in data mining for this context are obtained.
URI: http://hdl.handle.net/10397/63424
ISBN: 1-59140-057-0
Appears in Collections:Book Chapter

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

22
Last Week
0
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.