Incorporating Feature Selection Method into Neural Network Techniques in Sales Forecasting of Computer Products
Sales forecasting of computer products is regarded as an important but difficult task since computer products are characterized by product variety, rapid specification changes and rapid price declines. Artificial neural networks (ANNs) have been found to be useful techniques for sales forecasting. However the inability to identify important forecasting variables is one of the main shortcomings of ANNs. For selecting an appropriate number of forecasting variables which can best improve the performance of the neural network prediction model, a commonly discussed data mining technique, multivariate adaptive regression and splines (MARS), is adapted in this study. The proposed model, firstly, uses the MARS to select important forecasting variables. The obtained significant variables are then served as the inputs for two neural network models-support vector regression (SVR) and cerebellar model articulation controller neural network (CMACNN). A real sales data collected from a Taiwanese computer dealer is used as an illustrative example. Experimental results showed that the obtained important variables from MARS can improve the forecasting performance of the SVR and CMACNN models. The proposed two-stage forecasting models provide good alternatives for sales forecasting of computer products.
KeywordsSales forecasting computer product feature selection multivariate adaptive regression splines artificial neural networks
Unable to display preview. Download preview PDF.
- 14.Wu, J.Y., Lu, C.J.: Applying classification problems via a data mining approach based on a cerebellar model articulation controller. In: 1st Asian Conference on Intelligent Information and Database Systems, Dong Hoi City, Vietnam, pp. 61–66 (2009)Google Scholar
- 15.Wood, S.: Float Analysis: Powerful Technical Indicators using Price and Volume. John Wiley & Sons, New York (2002)Google Scholar