Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance Learning (MIL) to overcome this issue, and propose a new framework called MILLET: Multiple Instance Learning for Locally Explainable Time series classification. We apply MILLET to existing deep learning TSC models and show how they become inherently interpretable without compromising (and in some cases, even improving) predictive performance. We evaluate MILLET on 85 UCR TSC datasets and also present a novel synthetic dataset that is specially designed to facilitate interpretability evaluation. On these datasets, we show MILLET produces sparse explanations quickly that are of higher quality than other well-known interpretability methods. To the best of our knowledge, our work with MILLET, which is available on GitHub (https://github.com/JAEarly/MILTimeSeriesClassification), is the first to develop general MIL methods for TSC and apply them to an extensive variety of domains

翻译：传统时间序列分类方法通常是黑箱模型，难以对其决策过程进行内在解释。本研究利用多实例学习克服这一问题，提出名为MILLET（多实例学习用于局部可解释时间序列分类）的新框架。我们将MILLET应用于现有深度学习时间序列分类模型，证明其在不牺牲预测性能（某些情况下甚至更优）的前提下实现了内在可解释性。我们在85个UCR时间序列分类数据集上评估MILLET，同时引入专为可解释性评估设计的新型合成数据集。实验表明，MILLET能快速生成稀疏且质量优于其他主流可解释方法的解释。据我们所知，这是首个为时间序列分类开发通用多实例学习方法，并在广泛领域进行验证的工作（代码已开源：https://github.com/JAEarly/MILTimeSeriesClassification）。