Intelligent sales forecasting for fashion retailing using harmony search algorithms and extreme learning machines

Wai Keung Wong, Z. X. Guo

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

2 Citations (Scopus)

Abstract

A hybrid intelligent (HI) model, comprising a data pre-processing component and a HI forecaster, is developed to tackle the medium-term fashion sales forecasting problem. The HI forecaster 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. The learning algorithm integrates an improved harmony search algorithm and an extreme learning machine. Experiments based on real fashion retail data and public benchmark data sets were conducted to evaluate the performance of the proposed model. Results demonstrate that the performance is far superior to traditional autoregressive integrated moving average (ARIMA) models and two recently developed neural network models.
Original languageEnglish
Title of host publicationOptimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)
Subtitle of host publicationFrom Production to Retail
PublisherElsevier Inc.
Pages170-195
Number of pages26
ISBN (Print)9780857097798
DOIs
Publication statusPublished - 1 Jan 2013

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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