基于互相关的二阶段时间序列聚类方法

摘要

提出了一种高效的时间序列聚类方法，以互相关函数为基础，通过二阶段的方法实现更低时间复杂度下的时间序列聚类。第一步以时间序列符号化为基础，通过设计符号化序列特征抽取算法，抽取特征时间段；第二步以互相关函数为基础，通过改进的互相关函数步骤，实现更快速的时间序列聚类。实验结果表明，该方法可以适应稀疏及密集的时间序列数据抽取，同时与传统的聚类距离公式相比，处理速度更快，对时间序列形状的缩放有更好的表示效果，并能保持较高准确性。%Based on cross-correlation, an efficient, fast method is proposed for time series clustering and the time series clustering is realized by a two steps measure. The first step is based on symbolic of time series and extracts the characteristic time period by designing a characteristic extraction algorithm. The second step is based on cross-correlation, which realizes a faster time series clustering by adjusting the cross-correlation step. The experiments show that this method can fit sparse and dense time series data extraction. Comparing with traditional clustering distance measure, this method has high pro-cessing speed and can perform better on the stretch of time series shape. Meanwhile, this method keeps the accuracy in a high degree.