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The optimized GPM(1,1) for forecasting small sample oscillating series

机译:优化的GPM(1,1)用于预测小样本振荡序列

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This paper puts forward a modeling approach for oscillating series based on an optimized grey power model with first-order one-variable (abbreviated as GPM(1,1). An optimization method is used to determine the initial value in GPM(1,1) model, and furthermore, the parameters in the model are optimized by utilizing a non-linear programming model. The results show that the modeling approach proposed in this paper can reflect the fluctuation of original data and be solved handily using a computer. The range of application of Grey Model is further extended to forecast oscillating series. The effectiveness of the optimized GPM(1,1) model is demonstrated by an actual case study.
机译:本文提出了一种基于具有一流单变量的优化灰色电源模型的振荡系列建模方法(缩写为GPM(1,1)。优化方法用于确定GPM中的初始值(1,1 )模型和此外,通过利用非线性编程模型来优化模型中的参数。结果表明,本文提出的建模方法可以反映原始数据的波动,并使用电脑省地解决。范围灰色模型的应用进一步扩展到预测振荡系列。通过实际案例研究证明了优化的GPM(1,1)模型的有效性。

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