基于特征点分段的多元时间序列相似性搜索

现有的各种多元时间序列相似性搜索方法难以准确高效地完成搜索任务。提出了一种基于特征点分段的多元时间序列相似性搜索算法,提取所定义的用于分段的特征点,分段后将原时间序列转化为模式序列,该模式序列能够很好地保留原序列的全局形状特征,再用分层匹配的方法进行相似性搜索。实验结果表明,该方法能够有效刻画序列的全局形状特征,通过分层匹配保留局部的相似性,同时提高搜索准确率。

Similarity search algorithm for multivariate time series based on feature points

A variety of methods for matching multivariate time series can not measure similarity accurately and efficiently at the same time.This paper proposes a similarity search algorithm by segmentation based on special points of multivariate time series.It extracts the feature points,by which it segments the multivariate time series,and then transforms them into pattern sequences,thus the global shape characteristics of the original sequences can be retained.It makes the similarity search with the hierarchical matching method.The experimental results show that this method can effectively portray the global shape features of the sequences,retain local similarity matching by hierarchical matching,and improve the accuracy of the search at the same time.