In the research area of time series classification, the ensemble shapelet transform algorithm is one of the state-of-the-art algorithms for classification. However, its high time complexity is an issue to hinder its application since its base classifier shapelet transform includes a high time complexity of a distance calculation and shapelet selection. Therefore, in this paper we introduce a novel algorithm, i.e., short isometric shapelet transform (SIST), which contains two strategies to reduce the time complexity. The first strategy of SIST fixes the length of shapelet based on a simplified distance calculation, which largely reduces the number of shapelet candidates as well as speeds up the distance calculation in the ensemble shapelet transform algorithm. The second strategy is to train a single linear classifier in the feature space instead of an ensemble classifier. The theoretical evidence of these two strategies is presented to guarantee a near-lossless accuracy under some preconditions while reducing the time complexity. Furthermore, empirical experiments demonstrate the superior performance of the proposed algorithm.

在时间序列分类研究领域，集成shapelet变换算法是目前最先进的分类算法之一。然而，其高时间复杂度是阻碍其应用的问题，因为其基分类器shapelet变换包括距离计算和shapelet选择的高时间复杂度。因此，在本文中，我们引入了一种新算法，即短等距shapelet 变换（SIST），它包含两种降低时间复杂度的策略。SIST的第一种策略基于简化的距离计算来固定shapelet的长度，这大大减少了shapelet候选的数量，并加快了集成shapelet变换算法中的距离计算。第二种策略是在特征空间中训练单个线性分类器而不是集成分类器。提出了这两种策略的理论证据，以保证在某些前提下接近无损的准确性，同时降低时间复杂度。此外，经验实验证明了所提出算法的优越性能。