A multivariate intelligent decision-making model for retail sales forecasting

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.

► We investigate the relations between influencing factors and overall sales.
► A multivariate intelligent decision-making model is proposed to forecast sales.
► A harmony search-based variable selection method is proposed.
► The proposed model works well no matter whether historical sales data exist.