As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.

中文翻译：

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通过惰性时间序列分类发现Shapelet

作为区分性特征的代表，时间序列小波形最近受到了相当大的研究兴趣。但是，大多数基于小波的分类模型会评估小波在整个训练数据集上的差分能力，而忽略了要分类的每个实例中包含的特征信息和分类特征频率信息。因此，特征提取的计算复杂度高，并且可解释性不足。为此，通过融合全局和局部相似性的惰性策略提高了Shapelet发现的效率。在预测过程中，该策略为每个实例学习一个特定的评估数据集，然后直接使用捕获的特征来逐步减少预测类别标签的不确定性。此外，小波覆盖率得分被定义为计算每个时间戳对于不同类别的可分辨性。实验结果表明，所提出的方法与基准方法相比具有竞争力，并且可以深入了解数据中每个时间序列和每种类型的判别特征。