Accelerating Time Series Shapelets Discovery with Key Points
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.
KeywordsTime series Shapelets Classification Key points
This work is partially supported by National 863 Program of China under Grant No. 2015AA015401, Tianjin Municipal Science and Technology Commission under Grant No. 14JCQNJC00200, 13ZCZDGX01098, as well as Research Foundation of Ministry of Education and China Mobile Under Grant No. MCM20150507. This work is also partially supported by Jilin NSF Under Grant No. 20130101179JC-18 and YBU development plan 2014–16.
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