Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Development of a fuzzy sales forecasting system for vending machines
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
An introduction to variable and feature selection
The Journal of Machine Learning Research
A hybrid sales forecasting system based on clustering and decision trees
Decision Support Systems
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Expert Systems with Applications: An International Journal
Designing a decision-support system for new product sales forecasting
Expert Systems with Applications: An International Journal
Water demand forecasting using Kalman filtering
ASM '07 The 16th IASTED International Conference on Applied Simulation and Modelling
Evolving neural network for printed circuit board sales forecasting
Expert Systems with Applications: An International Journal
A sales forecasting model for new-released and nonlinear sales trend products
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Pairwise issue modeling for negotiation counteroffer prediction using neural networks
Decision Support Systems
Looking for representative fit models for apparel sizing
Decision Support Systems
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A sales forecasting problem in the retail industry is addressed based on early sales. An effective multivariate intelligent decision-making (MID) model is developed to provide effective forecasts for this problem by integrating a data preparation and preprocessing module, a harmony search-wrapper-based variable selection (HWVS) module and a multivariate intelligent forecaster (MIF) module. The HWVS module selects out the optimal input variable subset from given candidate inputs as the inputs of MIF. The MIF is established to model the relationship between the selected input variables and the sales volumes of retail products, and then utilized to forecast the sales volumes of retail products. Extensive experiments were conducted to validate the proposed MID model in terms of extensive typical sales datasets from real-world retail industry. Experimental results show that it is statistically significant that the proposed MID model can generate much better forecasts than extreme learning machine-based model and generalized linear model do.