Decision Sciences Journal
Volume 31, Number 4
Fall 2000
Marketing Category Forecasting: An Alternative of BVAR—Artificial
Neural Networks
James J. Jiang
Department of Computer Information Systems, College of Administration
and Business, Louisiana Tech University, Ruston, LA 71272, e-mail:
jiang@cab.latech.edu
Maosen Zhong
Department of Business Administration, School of Business, The
University of Texas at Brownsville, Brownsville, TX 78520, e-mail:
mzhong@utb1.utb.edu
Gary Klein
College of Business and Administration, The University of Colorado,
Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs,
CO 80933-7150, e-mail: gklein@mail.uccs.edu
ABSTRACT. Analyzing scanner data in brand management
activities presents unique difficulties due to the vast quantity
of the data. Time series methods that are able to handle the
volume effectively often are inappropriate due to the violation
of many statistical assumptions in the data characteristics.
We examine scanner data sets for three brand categories and examine
properties associated with many time series forecasting methods.
Many violations are found with respect to linearity, normality,
autocorrelation, and heteroscedasticity. With this in mind we
compare the forecasting ability of neural networks that require
no assumptions to two of the more robust time series techniques.
Neural networks provide similar forecasts to Bayesian vector
autoregression (BVAR), and both outperform generalized autoregressive
conditional herteroscedasticty (GARCH) models.
Subject Areas: Artificial Intelligence, Pricing, Sales
Analysis, and Statistics. |