Discovering shapelets with key points in time series classification

• Published in: Expert systems with applications 2019-10, Vol.132, p.76-86
• Main Authors: Li, Guiling, Yan, Wenhe, Wu, Zongda
• Format: Article
• Language: English
• Subjects: Classification; Decision trees; Key point; Performance evaluation; Pruning; Shapelet; Time series
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.04.062

•A new method is proposed to detect key points in the time series.•Extracting shapelets based on key points and pruning redundant shapelets.•Experiments on real datasets verify effectiveness and efficiency of our proposal. Shapelet is a time series subsequence that can best represent the time series of one class. Shapelet can improve the accuracy and efficiency of classification, as well as the interpretability of classification results. Although shapelet has good classification performance, how to efficiently find the optimal shapelet is still an important challenge due to the large number of shapelet candidates contained in a time series. In this paper, a new shapelet discovery method, referred to as Pruning Shapelets with Key Points (PSKP), is proposed. PSKP first finds the key points in time series according to the standard deviation of each time tick of time series, and then extracts shapelet candidates with these key points. Finally, PSKP classifies the time series through a decision tree constructed based on the optimal shapelet. We make experiments on various data sets and evaluate the performance of the proposed method with compared candidates. The experimental results demonstrate that the proposed method is feasible and effective.