2025/11/04 by Bartłomiej Małkus, Szymon Bobek, Małkus, Bartłomiej +3
Computer Science · Decision Sciences · #Autoencoder #Feature (linguistics) #Interpretability #Machine Learning in Healthcare #Multivariate statistics #Pattern recognition (psychology) #Series (stratigraphy) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting #Time series #Univariate #cs.LG
paper · pdf · doi:10.1007/s10618-026-01227-6
published in Data Mining and Knowledge Discovery 40(5) (Springer Science+Business Media)
openalex publication_date 2026/07/03 · openalex created_date 2026/07/04 · openalex updated_date 2026/07/28
Abstract Time series data is one of the most popular data modalities in critical domains such as industry and medicine. The demand for algorithms that not only exhibit high accuracy but also offer interpretability is crucial in such fields, as decisions made there bear significant consequences. In this paper, we present ProtoTSNet, a novel approach to interpretable classification of multivariate time series data, through substantial enhancements to the ProtoPNet architecture. Our method is tailored to overcome the unique challenges of time series analysis, including capturing dynamic patterns and handling varying feature significance. Central to our innovation is a modified convolutional encoder utilizing group convolutions, pre-trainable as part of an autoencoder and designed to preserve and quantify feature importance. We evaluated our model on 30 multivariate time series datasets from the UEA archive, comparing our approach with existing explainable methods as well as non-explainable baselines. Through comprehensive evaluation and ablation studies, we demonstrate that our approach achieves the best performance among ante-hoc explainable methods while maintaining competitive performance with non-explainable and post-hoc explainable approaches, providing interpretable results accessible to domain experts.