Sun Z.-L.TSAN MING CHOIAu K.-F.Yu Y.2022-05-302022-05-302008https://www.scopus.com/inward/record.uri?eid=2-s2.0-56049098499&doi=10.1016%2fj.dss.2008.07.009&partnerID=40&md5=eca9f9ee26dd9f2d27592f77776a5825https://scholars.lib.ntu.edu.tw/handle/123456789/612405Sales 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. ? 2008 Elsevier B.V. All rights reserved.Artificial neural network; Backpropagation neural networks; Decision support system; Extreme learning machine; Fashion sales forecastingAdministrative data processing; Artificial intelligence; Backpropagation; Decision support systems; Decision theory; Forecasting; Image classification; Learning systems; Machine design; Management information systems; Sales; Vegetation; Artificial neural network; Backpropagation neural networks; Decision support system; Extreme learning machine; Fashion sales forecasting; Neural networksjournal article10.1016/j.dss.2008.07.0092-s2.0-56049098499