In recent years, early classification on time series has become increasingly important in time-sensitive applications. Existing shapelet based methods still cannot work well on this problem. First, the effectiveness of traditional shapelet based methods would be influenced by the number of shapelet candidates. Second, it is difficult for previous methods to obtain diverse shapelets in shapelet selection. In this paper, we propose an Improved Early Distinctive Shapelet Classification method named IEDSC. We first present a new method to more precisely measure the similarity between time series, which takes into account of the relative trend of time series. Second, in shapelet extraction, we propose a pruning technique to reduce the number of shapelets by predicting the starting positions of shapelets with good quality. In addition, a new shapelet selection method is also proposed to remove the similar shapelets, so as to maintain the diversity of shapelets. Finally, the experimental results on 16 benchmark datasets show that the proposed method outperforms state-of-the-art for early classification on time series.

近年来，对时间序列的早期分类在对时间敏感的应用中变得越来越重要。现有的基于shapelet的方法仍然无法很好地解决此问题。首先，传统基于小波的方法的有效性将受小波候选者数量的影响。第二，对于先前的方法，很难在小波选择中获得多样化的小波。在本文中，我们提出了一种改进的早期独特小波分类方法，称为IEDSC。我们首先提出一种新方法，该方法考虑到时间序列的相对趋势，可以更精确地测量时间序列之间的相似度。其次，在小块提取中，我们提出了一种修剪技术，通过预测高质量的小块的起始位置来减少小块的数量。此外，还提出了一种新的形状选择方法，以去除相似的形状，以保持形状的多样性。最后，在16个基准数据集上的实验结果表明，所提出的方法优于最新的时间序列分类方法。