Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22240
Title: A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm
Authors: Wong, WK 
Guo, ZX
Keywords: Extreme learning machine
Fashion sales forecasting
Harmony search
Neural network
Issue Date: 2010
Publisher: Elsevier
Source: International journal of production economics, 2010, v. 128, no. 2, p. 614-624 How to cite?
Journal: International journal of production economics 
Abstract: 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.
URI: http://hdl.handle.net/10397/22240
ISSN: 0925-5273
DOI: 10.1016/j.ijpe.2010.07.008
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

53
Last Week
0
Last month
0
Citations as of Aug 19, 2017

WEB OF SCIENCETM
Citations

37
Last Week
0
Last month
1
Citations as of Aug 21, 2017

Page view(s)

41
Last Week
4
Last month
Checked on Aug 21, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.