Points2Shapelets: A Salience-Guided Shapelets Selection Approach to Time Series Classification

In the field of data mining, Time Series Classification (TSC) has attracted lots of interest from researchers due to its wide range of applications. Recently, deep learning models have shown promising performance on TSC by automatically extracting discriminative features from original time series. However, deep learning models are very time-consuming for training and lack interpretability. In the contrast, shapelets-based algorithms are easy to be implemented with interpretability preserved. In this paper, we proposed a salience-guided shapelets selection approach named Points2Shapelets, which inherits the advantages of both deep learning-based models and shapelets-based algorithms for TSC. Specifically, an interpretable and transparent shapelets selection approach is designed by leveraging the salience analysis of the pre-trained model. By conducting comprehensive experiments on 20 public UCR time series datasets, the experimental results demonstrate that our proposed approach Points2Shapelets achieves competitive performance shown by deep learning-based models. Meanwhile, explanations about how the salience analysis guides the decision-making of TSC models are offered in a visually understandable manner.