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Discrete Grey Forecasting Model And Its Optimization

机译：离散灰色预测模型及其优化

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Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proposes a novel discrete grey forecasting model termed DGM model and a series of optimized models of DGM. This paper modifies the algorithm of GM(1,1) model to enhance the tendency catching ability. The relationship between the two models and the forecasting precision of DGM model based on the pure index sequence is discussed. And further studies on three basic forms and three optimized forms of DGM model are also discussed. As shown in the results, the proposed model and its optimized models can increase the prediction accuracy. When the system is stable approximately, DGM model and the optimized models can effectively predict the developing system. This work contributes significantly to improve grey forecasting theory and proposes more novel grey forecasting models.
机译：尽管灰色预测模型已在各个领域得到成功采用并显示出可喜的成果，但文献表明其性能可以进一步提高。为此，本文提出了一种新颖的离散灰色预测模型，称为DGM模型和一系列DGM优化模型。本文对GM（1,1）模型的算法进行了改进，以增强趋势捕获能力。讨论了两种模型之间的关系以及基于纯索引序列的DGM模型的预测精度。并讨论了DGM模型的三种基本形式和三种优化形式的进一步研究。结果表明，提出的模型及其优化模型可以提高预测精度。当系统大致稳定时，DGM模型和优化模型可以有效地预测开发系统。这项工作为改进灰色预测理论做出了重要贡献，并提出了更多新颖的灰色预测模型。

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