Sales forecasting using extreme learning machine with applications in fashion retailing

Zhan Li Sun, Tsan Ming Choi, Kin Fan Au, Yong Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

370 Citations (Scopus)


Sales forecasting is a challenging problem owing to the volatility of demand which depends on many factors. This is especially prominent in fashion retailing where a versatile sales forecasting system is crucial. This study applies a novel neural network technique called extreme learning machine (ELM) to investigate the relationship between sales amount and some significant factors which affect demand (such as design factors). Performances of our models are evaluated by using real data from a fashion retailer in Hong Kong. The experimental results demonstrate that our proposed methods outperform several sales forecasting methods which are based on backpropagation neural networks.
Original languageEnglish
Pages (from-to)411-419
Number of pages9
JournalDecision Support Systems
Issue number1
Publication statusPublished - 1 Dec 2008


  • Artificial neural network
  • Backpropagation neural networks
  • Decision support system
  • Extreme learning machine
  • Fashion sales forecasting

ASJC Scopus subject areas

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


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