In the last decade, there has been significant progress in time series classification. However, in real-world industrial settings, it is expensive and difficult to obtain high-quality labeled data. Therefore, the positive and unlabeled learning (PU-learning) problem has become more and more popular recently. The current PU-learning approaches of the time series data suffer from low accuracy due to the lack of negative labeled time series. In this paper, we propose a novel shapelet based two-step (2STEP) PU-learning approach. In the first step, we generate shapelet features based on the positive time series, which are used to select a set of negative examples. In the second step, based on both positive and negative time series, we select the final features and build the classification model. The experimental results show that our 2STEP approach can improve the average F1 score on 15 datasets by 9.1% compared with the baselines, and achieves the highest F1 score on 10 out of 15 time series datasets.

在过去的十年中，时间序列分类取得了重大进展。然而，在现实世界的工业环境中，获得高质量的标记数据既昂贵又困难。因此，正向无标签学习（PU-learning）问题最近变得越来越流行。由于缺乏负标记时间序列，当前时间序列数据的 PU 学习方法的准确性较低。在本文中，我们提出了一种新颖的基于 shapelet 的两步 (2STEP) PU 学习方法。第一步，我们根据正时间序列生成 shapelet 特征，用于选择一组负样本。第二步，基于正负时间序列，选择最终特征并构建分类模型。实验结果表明，我们的2STEP方法可以将15个数据集上的平均F 1 分数与基线相比提高9.1%，并且在15个时间序列数据集中的10个上实现最高的F 1 分数。