By Topic

A Hybrid Subspace-Connectionist Data Mining Approach for Sales Forecasting in the Video Game Industry

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Marcoux, J. ; Univ. de Moncton, Moncton, NB, Canada ; Selouani, S.

This paper addresses the issue of sales forecasting using a new approach based on connectionist and subspace decomposition methods.A tool is designed to support company management in the process of determining expected sales figures. Neural networks trained with a back-propagation algorithm are used to predict the weekly sales of a video game. For this purpose, optimal topology is found and a time-sensitive neural network is implemented. We have considered the use of many influencing indicators and parameters as inputs. In order to assess the relevance of these parameters, we perform a pre-processing based on Principal Component Analysis. The performance of the proposed system is evaluated and compared with baseline reference sales. The results are presented and discussed with regards to prediction accuracy.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009