Forecasting brand sales with wavelet decompositions of related causal series Online publication date: Thu, 17-Sep-2009
by Antonis A. Michis
International Journal of Business Forecasting and Marketing Intelligence (IJBFMI), Vol. 1, No. 2, 2009
Abstract: We consider methods for forecasting brand sales utilising wavelet decompositions of related causal series. Wavelet decompositions can uncover the hidden periodicities inherent in marketing time series like pricing and can therefore provide superior information in causal sales forecasting methods. We specifically address the problem of multicollinearity since the proposed wavelet packet transformation of a time series of length T, generates 2T – 2 correlated vectors of coefficients, each of length T. We find that partial least-squares provide the most accurate forecasting method which at the same time achieves the desired dimension reduction in the estimation problem.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Forecasting and Marketing Intelligence (IJBFMI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com