Sales forecasting using extreme learning machine with applications in fashion retailing

  • Authors:
  • Zhan-Li Sun;Tsan-Ming Choi;Kin-Fan Au;Yong Yu

  • Affiliations:
  • Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

  • Venue:
  • Decision Support Systems
  • Year:
  • 2008

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Abstract

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.