An Interpretable Time Series Classification Approach Based on Feature Clustering

The time series classification problem has been an important mining task and applied in many real-life applications. A large number of approaches have been proposed, including shape-based approaches, dictionary-based ones, ensemble-based ones and some deep-learning approaches. However, these approaches either suffer from low accuracy or need massive features which hinder the interpretability. To overcome these challenges, in this paper, we propose a novel approach, FCCA, based on the feature clustering. We first present the formal definition features of various types. Then we propose the approaches of feature candidates generation, feature filtering and feature clustering. With a small number of representative features, FCCA not only achieves high accuracy, but also improves the interpretability greatly. Extensive experiments are conducted on UCR benchmark to verify the effectiveness and efficiency of the proposed approach.