特征点分段提取的时间序列模式匹配方法

Segmentation extraction of feature points for time series pattern matching

常用的时间序列模式匹配方法难以平衡计算复杂度与匹配精度,针对该问题,提出了一种特征点分段提取的时间序列模式匹配方法。提取时间序列每个变量维度上的特征点,降低序列长度;将特征点序列转化为分位点矩阵,利用欧氏距离对分位点矩阵进行相似性度量;在几组时间序列数据集上对所提方法进行分类实验。结果表明:所提方法在降低计算复杂度的同时,获得了较高的匹配精度。

It is difficult for the common time series pattern matching methods to balance the computational complexity and matching accuracy.To solve this problem,a time series matching method based on segmented extraction of feature points is proposed.Firstly,the feature points on each variable dimension of the time series are extracted and the sequence length is compressed.Then,the quantile matrix is calculated according to the feature sequence,and the similarity of the quantile matrix is measured by Euclidean distance.Finally,the effectiveness of the proposed method is verified on the application data set.Experimental results show that the proposed method can effectively reduce the computational complexity and ensure high matching accuracy.