 Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecastingChi-Jie Lu and Yen-Wen Wang International Journal of Production Economics, 2010, vol. 128, issue 2, 603-613 Abstract: In the evaluation of supply chain process improvements, the question of how to predict product demand quantity and prepare material flows in order to reduce cycle time has emerged as an important issue, especially in the 3C (computer, communication, and consumer electronic) market. This paper constructs a predicting model to deal with the product demand forecast problem with the aid of a growing hierarchical self-organizing maps and independent component analysis. Independent component analysis method is used to detect and remove the noise of data and further improve the performance of predicting model, then growing hierarchical self-organizing maps is used to classify the data, and after the classification, support vector regression is applied to construct the product demand forecasting model. In the experimental results, the model proposed in this paper can be successfully applied in the forecasting problem. Keywords: Demand; forecasting; Support; vector; regression; Independent; component; analysis; Growing; hierarchical; self-organizing; maps (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:128:y:2010:i:2:p:603-613 Access Statistics for this article International Journal of Production Economics is currently edited by Stefan Minner More articles in International Journal of Production Economics from Elsevier |
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