An envelopment learning procedure for improving prediction accuracies of grey models

Because the lifecycles of consumable electronic products are now very short, it has become very difficult for manufacturers to precisely determine customer demands with limited historical data. Over the past two decades, the grey model (GM) and its extensions have been shown to be effective tools to deal with short-term time series data. To further enforce the effectiveness of data uncertainty treatment for dynamic integrated-circuit assembly industries, a GM envelopment learning procedure is developed. In our procedure, short term series data is fuzzified to form a fuzzy time series for the purpose of building GM models, in which the final predictions are further aggregated with the proposed weights. The experimental results of a real case and a public dataset indicate that the proposed procedure can further improve the accuracy of predictions given by GM models and thus has practical value in tackling real cases.