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The optimal combination forecasting model based on closeness degree and IOWHA operator under the uncertain environment

机译:不确定环境下基于亲近度和IOWHA算子的最优组合预测模型

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We propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA) operator under the uncertain environment in which the raw data are expressed as interval numbers. It is a new kind of combination forecasting model with variant weights. We can obtain weighted coefficient vectors of combination forecasting methods by maximizing the closeness degree of combination forecasting, instead of minimizing absolute errors. We put forward the new concepts of closeness degree for the center and radius of interval numbers sequences, and construct the optimal interval combination forecasting model to maximize the sum of convex combination with closeness degree of interval center and closeness degree of interval radius. Furthermore, we discuss the solution to the model. Finally, an example is used to show that this model can improve the combination forecasting accuracy efficiently compared with each individual forecasting method.
机译:我们提出了基于近度度的最佳组合预测模型,并在不确定环境下引起有序加权谐波平均(IOWHA)操作员,其中原始数据表示为间隔数。它是一种具有变体重量的新型组合预测模型。通过最大化组合预测的近距离,可以获得组合预测方法的加权系数向量,而不是最小化绝对误差。我们提出了间隔数序列的中心和半径的近距离的新概念,并构建了最佳区间组合预测模型,以使凸组合与间隔中心的近距离和间隔半径的近度度的总和最大化。此外,我们讨论了模型的解决方案。最后,使用示例来表明该模型可以与每个预测方法有效地提高预测准确性。

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