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.
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.
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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