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中文摘要：
由于形态特征能够较为客观地反映时间序列的变化趋势，在时间序列数据降维过程中，形态特征的提取能够保留较为充分的数据信息，为提高后期的时序数据挖掘的效率提供可靠的保障．文中提出基于形态特征的时间序列符号聚合近似方法，综合考虑分段序列的均值和数据分布的形态特征，并且通过论域转化对它们实现符号转化．在相同的压缩比环境下，与传统符号化表示方法相比，该方法能更好地提供原始时间序列数据信息，进而提高时间序列数据挖掘的效率．
英文摘要：
Changeable trends of time series can be reflected by shape features which retain sufficient data information during the dimensionality reduction. It is good to improve the efficiency of time series data mining in the later stage. A symbolic aggregate approximation based on shape features is proposed. It regards the mean and the shape feature of a sequence as two important characteristics, and changes their domains of discourse to transform them into strings. Compared with the traditional methods, the proposed method improves the efficiency of time series data mining in the setting of equal compress rate because of the sufficient information which is retained by the previous stage.
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