不同粒度时间序列相似性度量

Similarity measurement of time-series data with different granularities

现有的时间序列的相似性度量大多基于欧氏距离,并不适用于不同粒度时间序列的相似性匹配,无法直接对其相似性进行有效的度量,为此,提出一种基于对应差值比样本的相似性度量,用于不同粒度时间序列的相似性匹配。首先对不同时间粒度的时序数据进行阐述,并定义了对应差值比样本与相似度计算方法;接着提出基于它们的相似性匹配算法;最后实验证明,该度量能够有效地度量不同粒度时间序列数据的相似性。

Most of the existing similarity measurement,based on Euclidean distance,cannot be applied directly and effectively to similarity matching of the time-series with different granularities.This paper proposed a new similarity measure based on the sample of the corresponding D-value.It firstly expounded the definition of the time-series with different granularities,and defined the sample of the corresponding D-value;secondly it put forward the similarity matching algorithm;finally,the experimental results prove that the algorithm can effectively measure the similarity of time-series with multiple granularities.