符号化聚集近似是一种有效的时间序列数据离散化降维方法,为了扩展非等维符号化时间序列相似性度量的解决方案,提出了一种新方法.首先将关键点提取技术应用在符号化算法中对时间序列进行降维处理,然后利用文中提出的方法对非等长的时间序列进行局部等维处理,再符号化；最后采用不同的方法进行相似度对比计算.实验结果表明,这种方法是简单而有效的,并且使非等长符号化时间序列的相似性度量及聚类方法得到了拓展.%Symbolic aggregate approximation is an effective data discretization method which can reduce dimensionality of time series. But after dimensionality reduction, time series become unequal length. For extending the methods that is used in those time series, a new arithmetic is proposed. Firstly, in symbolic aggregate approximation method extracting key points technology is used to dimensional reduction. Secondly, unequal length key point time series is gotten got. At the same time, the proposed method is used to process those time series. After it, those time series become equal in local, and symbolization. At last, different similarity calculation methods are used in contrast. The experimental results show that this method is simple and effective, and which extends the methods of similarity calculation and clustering.
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