A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm

Wai Keung Wong, Z. X. Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

125 Citations (Scopus)


A hybrid intelligent (HI) model, comprising a data preprocessing component and a HI forecaster, is developed to tackle the medium-term fashion sales forecasting problem. The HI forecaster firstly adopts a novel learning algorithm-based neural network to generate initial sales forecasts and then uses a heuristic fine-tuning process to obtain more accurate forecasts based on the initial ones. The learning algorithm integrates an improved harmony search algorithm and an extreme learning machine to improve the network generalization performance. Extensive experiments based on real fashion retail data and public benchmark datasets were conducted to evaluate the performance of the proposed model. The experimental results demonstrate that the performance of the proposed model is much superior to traditional ARIMA models and two recently developed neural network models for fashion sales forecasting.
Original languageEnglish
Pages (from-to)614-624
Number of pages11
JournalInternational Journal of Production Economics
Issue number2
Publication statusPublished - 1 Dec 2010


  • Extreme learning machine
  • Fashion sales forecasting
  • Harmony search
  • Neural network

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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