Intelligent multivariate sales forecasting using wrapper approach and neural networks

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

This research investigated a retail sales forecasting problem based on early sales. An effective multivariate intelligent decision-making (MID) model is developed to handle 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 proposed to model the relationship between the selected input variables and the sales volumes of retail products, and then employed to forecast the sales volumes of retail products. Experiments were conducted to evaluate the effectiveness of the proposed model. Results show that it is statistically significant that the proposed MID model can provide superior forecasts to ELM-based model and generalized linear model.
Original languageEnglish
Title of host publicationINDIN 2012 - IEEE 10th International Conference on Industrial Informatics
Pages145-150
Number of pages6
DOIs
Publication statusPublished - 6 Nov 2012
EventIEEE 10th International Conference on Industrial Informatics, INDIN 2012 - Beijing, China
Duration: 25 Jul 201227 Jul 2012

Conference

ConferenceIEEE 10th International Conference on Industrial Informatics, INDIN 2012
Country/TerritoryChina
CityBeijing
Period25/07/1227/07/12

Keywords

  • early sales
  • multivariate forecasting
  • retail industry

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

Cite this