基于多样化top-k shapelets转换的时间序列分类方法

摘要

Focusing on the issue that shapelets candidates can be very similar in time series classitication by shapelets transform,a diversified top-k shapelets transform method named DivTopKShapelet was proposed.In DivTopKShapelet,the diversified top-k query method was used to filter similar shapelets and select the k most representative shapelets.Then the optimal shapelets was used to transform data,so as to improve the accuracy and time efficiency of typical time series classification method.Experimental results show that compared with clustering based shapelets classification method (ClusterShapelet) and coverage based shapelets classification method (ShapeletSelction),DivTopKShapelet method can not only improve the traditional time series classification method,but also increase the accuracy by 48.43％ and 32.61％ at most;at the same time,the proposed method can enhance the computational efficiency in 15 data sets,which is at least 1.09 times and at most 287.8 times.%针对基于shapelets转换的时间序列分类方法中候选shapelets存在较大相似性的问题,提出一种基于多样化top-k shapelets转换的分类方法DivTopKShapelet.该方法采用多样化top-k查询技术,去除相似shapelets,并筛选出最具代表性的k个shapelets集合,最后以最优shapelets集合为特征对数据集进行转换,达到提高分类准确率及时间效率的目的.实验结果表明,DivTopKShapelet分类方法不仅比传统分类方法具有更高的准确率,而且与使用聚类筛选的方法(ClusterShapelet)和shapelets覆盖的方法(ShapeletSelection)相比,分类准确率最多提高了48.43％和32.61％;同时在所有15个数据集上均有计算效率的提升,最少加速了1.09倍,最高可达到287.8倍.