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Study on grey prediction model GM(1,1) and its application based on amplitude compression transformation

机译:基于幅度压缩变换的灰色预测模型GM(1,1)及其应用研究

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摘要

With respect to the issue that traditional grey model GM(1,1) hardly satisfies the prediction requirement of sequence fluctuation, a new data processing method of dealing with amplitude compression transformation and average weighted transformation is proposed. Furthermore, the fact that amplitude compression transformation is able to decrease the fluctuation of sequences and average weighted transformation can enhance the smoothness of sequences proved theoretically. Grey prediction model GM(1,1) is established based on applying amplitude compression transformation and average weighted transformation to exchange the fluctuation sequences. Finally, an application to forecast China's extraordinarily serious road traffic accidents is presented to illustrate the proposed framework.
机译:关于传统灰色模型GM(1,1)几乎不满足序列波动的预测要求,提出了一种处理幅度压缩变换和平均加权变换的新数据处理方法。此外,幅度压缩变换能够降低序列的波动和平均加权变换的事实可以提高理论上证明的序列的平滑度。基于施加幅度压缩变换和平均加权变换来建立灰色预测模型Gm(1,1)以交换波动序列。最后,提出了一种预测中国非常严重的道路交通事故的申请表明拟议的框架。

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