Accelerating Time Series Shapelets Discovery with Key Points

摘要：

　　Shapelets are discriminative subsequences in a time series dataset,which provide good interpretability for time series classification results.For this reason,time series shapelets have attracted great interest in time series data mining community.Although time series shapelets have satisfactory performance on many time series datasets,how to fast discover them is still a challenge because any subsequence in a time series may be a shapelet candidate.There are several methods to speed up shapelets discovery in recent years.However,these methods are still time-consuming when dealing with the large datasets or long time series.In this paper,we propose a preprocessing step with time series key points for shapelets discovery which make full use of the prior knowledge of shapelets.Combining with shapelets discovery method based on SAX(Fast-Shaplets),we can find shapelets quickly on all benchmark datasets of UCR archives,while the classification accuracy is almost the same as the current methods.

关键词： Time series Shapelets Classification Key points

作者: Zhenguo Zhang Haiwei Zhang Yanlong Wen Xiaojie Yuan

作者单位: College of Computer and Control Engineering,Nankai University,38 Tongyan Road,Tianjin 300350,People College of Computer and Control Engineering,Nankai University,38 Tongyan Road,Tianjin 300350,People

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 330-342

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）