An intelligent model for predicting demand pattern

Wai Keung Wong, Z. X. Guo

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

Abstract

To handle the seasonal product demand forecasting problem, this paper develops an intelligent forecasting model integrating a harmony search algorithm, an extreme learning machine (ELM) and a heuristic finetuning process. The ELM combined with harmony search algorithm is used to provide effective initial forecasts. The heuristic fine-tuning process is then used to generate more accurate forecasts based on the initial forecasts. Extensive experiments based on real fashion retail data 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 two recently developed neural network models for seasonal product demand forecasting.
Original languageEnglish
Title of host publicationProceedings of the 5th IASTED International Conference on Computational Intelligence, CI 2010
Pages1-6
Number of pages6
Publication statusPublished - 1 Dec 2010
Event5th IASTED International Conference on Computational Intelligence, CI 2010 - Maui, HI, United States
Duration: 23 Aug 201025 Aug 2010

Conference

Conference5th IASTED International Conference on Computational Intelligence, CI 2010
Country/TerritoryUnited States
CityMaui, HI
Period23/08/1025/08/10

Keywords

  • Extreme learning machine
  • Harmony search
  • Neural network
  • Seasonal product demand forecasting

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

Cite this