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首页> 外文会议>IEEE International Conference on Grey Systems and Intelligent Services >A grey silhouette coefficient for the small sample forecasting
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A grey silhouette coefficient for the small sample forecasting

机译：小样本预测的灰色轮廓系数

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摘要

Small sample forecasting problem is an important issue in various fields. The early stage of manufacturing system is a positive example about this issue. Manufacturers need sufficient management knowledge to lower overall production cost, but it is a hard task due to the obtained samples is limited. This study is thus to develop a modeling procedure to acquire stable prediction results under small data sets. Briefly, we first judge some single models to determine whether the real sequence tendency can be reflected with the grey incidence analysis and then evaluate their forecasting stability by the relative ratio of error range; finally, the grey silhouette coefficient is developed to build an applicable hybrid forecasting model for small samples. The material fatigue limit data set is used here to confirm the effectiveness and practical application value of the proposed method. The empirical results show that the hybrid model indeed can lower forecasting errors and come up better results with the limited data. Consequently, the proposed procedure is considered a feasible tool for the small sample forecasting problem.
机译：小样本预测问题是各个领域中的重要问题。制造系统的早期阶段就是一个很好的例子。制造商需要足够的管理知识来降低总体生产成本，但是由于获得的样品有限，这是一项艰巨的任务。因此，本研究旨在开发一种建模程序，以在小数据集下获取稳定的预测结果。简而言之，我们首先判断一些单一模型，以确定是否可以通过灰色关联分析反映真实的序列趋势，然后通过误差范围的相对比率来评估其预测稳定性。最后，开发灰色轮廓系数以建立适用于小样本的混合预测模型。在此使用材料疲劳极限数据集来确认所提出方法的有效性和实际应用价值。实证结果表明，在数据有限的情况下，混合模型确实可以降低预测误差，得出更好的结果。因此，所提出的程序被认为是解决小样本预测问题的可行工具。

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