ProtoTSNet：基于原型部分的可解释多变量时间序列分类
ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts
AI总结：
针对多变量时间序列分类问题，提出ProtoTSNet方法，通过改进ProtoPNet架构，利用分组卷积的卷积编码器克服时间序列分析挑战，在30个数据集上评估，该方法在事前可解释方法中性能最佳，兼具与其他方法的竞争力并提供可解释结果。
摘要：
时间序列数据是工业和医学等关键领域中最流行的数据形式之一。在这些领域，不仅需要算法具有高精度，还需要具备可解释性，因为这些领域的决策后果重大。本文提出了ProtoTSNet，一种通过对ProtoPNet架构进行大幅改进，实现多变量时间序列数据可解释分类的新方法。该方法旨在克服时间序列分析的独特挑战，包括捕捉动态模式和处理不同特征的重要性。其核心创新在于改进的卷积编码器，利用分组卷积，可作为自编码器的一部分进行预训练，以保留和量化特征重要性。我们在来自UEA存档的30个多变量时间序列数据集上评估了该模型，将其与现有可解释方法以及不可解释基线进行比较。通过综合评估和消融研究，我们证明该方法在事前可解释方法中性能最佳，同时与不可解释和事后可解释方法保持有竞争力的性能，为领域专家提供了可解释的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
