Incorporating feature selection method into neural network techniques in sales forecasting of computer products

Authors:
Chi-Jie Lu;Jui-Yu Wu;Tian-Shyug Lee;Chia-Mei Lian
Affiliations:
Department of Industrial Engineering and Management, Ching Yun University, Taoyuan, Taiwan, R.O.C.;Department of Business Administration, Lunghwa University of Science and Technology, Taiwan, R.O.C.;Department of Business and Administration, Fu Jen Catholic University, Taiwan, R.O.C.;Graduate School of Business Administration, Fu Jen Catholic University, Taiwan, R.O.C.
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

Citing 10
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Practical selection of SVM parameters and noise estimation for SVM regression

Neural Networks
A hybrid sales forecasting system based on clustering and decision trees

Decision Support Systems
A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry

Decision Support Systems
A hybrid system by evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting

Decision Support Systems
A neural clustering and classification system for sales forecasting of new apparel items

Applied Soft Computing
Financial time series forecasting using independent component analysis and support vector regression

Decision Support Systems
Applying Classification Problems via a Data Mining Approach Based on a Cerebellar Model Articulation Controller

ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Computational Statistics & Data Analysis
A two-stage dynamic sales forecasting model for the fashion retail

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.