A multivariate intelligent decision-making model for retail sales forecasting

Z. X. Guo, Wai Keung Wong, Min Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

54 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)247-255
Number of pages9
JournalDecision Support Systems
Issue number1
Publication statusPublished - 1 Apr 2013


  • Early sales
  • Multivariate forecasting
  • Retail industry
  • Sales forecasting

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Information Systems and Management
  • Arts and Humanities (miscellaneous)
  • Developmental and Educational Psychology


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