ProtoPGTN: A Scalable Prototype-Based Gated Transformer Network for Interpretable Time Series Classification

Time Series Classification (TSC) plays a crucial role in machine learning applications across domains such as healthcare, finance, and industrial systems. In these domains, TSC requires accurate predictions and reliable explanations, as misclassifications may lead to severe consequences. In addition, scalability issues, including training time and memory consumption, are critical for practice usage. To address these challenges, we propose ProtoPGTN, a prototype-based interpretable framework that unifies gated transformers with prototype reasoning for scalable time series classification. Unlike existing prototype-based interpretable TSC models which rely on recurrent structure for sequence processing and Euclidean distance for similarity computation, ProtoPGTN adapts Gated Transformer Networks (GTN), which uses an attention mechanism to capture both temporal and spatial long-range dependencies in time series data and integrates the prototype learning framework from ProtoPNet with cosine similarity to enhance metric consistency and interpretability. Extensive experiments are conducted on 165 publicly available datasets from the UCR and UEA repositories, covering both univariate and multivariate tasks. Results show that ProtoPGTN obtains at least the same performance as existing prototype-based interpretable models on both multivariate and univariate datasets. The average accuracy on multivariate and univariate datasets stands at 67.69% and 76.99%, respectively. ProtoPGTN achieves up to 20× faster training and up to 200× lower memory consumption than existing prototype-based interpretable models.