Time series shapelets are subsequences of time series that could be representative of a class. Shapelets-based time series classification methods can be divided into two large categories. The first category integrates shapelets selection within the process of constructing classifier; while the second category disconnects the process of finding shapelets from the classification algorithm by adopting a shapelet transformation. However, there are two important limitations of shapelet transformation. First, the number of shapelets selected for transformation has great influence on classification result, but it is difficult to decide the quantity of shapelets which yields the best data for classification. Second, similar shapelets always exist among the selected shapelets in previous algorithms. In our work, the latter problem is addressed by introducing an efficient and effective pruning technique, it filters similar shapelets and decreases the number of candidate shapelets at the same time. Then, we propose a novel shapelet coverage method to select shapelets for a given dataset. The final selected shapelets are named after Discriminative Shapelets. Our experimental results demonstrate that, on the classic benchmark datasets used for time series classification, shapelet pruning and coverage method outperforms ShapeletFilter.
