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Los Angeles, CA
March 31, 2009 to April 2, 2009
ISBN: 978-0-7695-3507-4
pp: 666-670
ABSTRACT
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.
INDEX TERMS
Sales forecasting, Neural networks, Principal component analysis, Data mining, Video games
CITATION
Julie Marcoux, Sid-Ahmed Selouani, "A Hybrid Subspace-Connectionist Data Mining Approach for Sales Forecasting in the Video Game Industry", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 666-670, doi:10.1109/CSIE.2009.1001
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