根据灰色系统理论和序列数据的特性，提出一种灰插值方法。

Combining grey system theory with the feature of series data, a grey interpolation approach based on forward and back grey prediction model is proposed.

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分别在单序列时建立递进灰拟合模型，在多维数据集时利用属性相关性，对插值结果进行学习优化。

An optimization method for reasoning results is presented, such as recursive grey fitting model for single sequence and attribute correlativity model for multi-dimension data.

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