Discovering shapelets with key points in time series classification
Discovering shapelets with key points in time series classification
复制标题
发现具有时间序列分类关键点的 shapelet
DOI：
10.1016/j.eswa.2019.04.062
复制
发表时间：
2019-10-15
影响因子：
8.5
通讯作者：
Wu, Zongda
中科院分区：
文献类型：
--
作者：
Li, Guiling;Yan, Wenhe;Wu, Zongda
Shapelet是一个时间序列子序列，可以最好地表示一类时间序列。Shapelet可以提高分类的精度和效率，以及分类结果的可解释性。虽然shapelet具有良好的分类性能，如何有效地找到最佳的shapelet仍然是一个重要的挑战，由于大量的shapelet候选人包含在一个时间序列。本文提出了一种新的Shapelet发现方法，称为关键点剪枝Shapelet（PSKP）。PSKP首先根据时间序列每个时间刻度的标准差找到时间序列中的关键点，然后用这些关键点提取shapelet候选。最后，PSKP通过基于最优shapelet构造的决策树对时间序列进行分类。我们在不同的数据集上进行实验，并与比较候选人评估所提出的方法的性能。实验结果表明，该方法是可行和有效的。(C)2019爱思唯尔有限公司版权所有。
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. (C) 2019 Elsevier Ltd. All rights reserved.