  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
