Shapelet selection for time series classification

Abstract

In recent times, increasing attention has been given to shapelet-based methods for time series classification. However, in the majority of current methods, similar subsequences were often selected as shapelets, thereby reducing the final interpretability of these methods. Aiming to circumvent the selection of similar subsequences as the final shapelets, a novel shapelet selection method (SSM) was proposed in this paper. Firstly, shapelet candidates were generated by SSM through time series segmentation to avoid excessive generation of similar candidates from a single time series. Secondly, all shapelet candidates were evaluated simultaneously to improve evaluation efficiency. Finally, SSM introduced a position-based filter to prevent the selection of similar sequences repeatedly. The results obtained on the UCR TSC archive demonstrated the effectiveness of the proposed method.