  Exploring the expansion history of the universe, understanding its
evolutionary stages, and predicting its future evolution are important goals in
astrophysics. Today, machine learning tools are used to help achieving these
goals by analyzing transient sources, which are modeled as uncertain time
series. Although black-box methods achieve appreciable performance, existing
interpretable time series methods failed to obtain acceptable performance for
this type of data. Furthermore, data uncertainty is rarely taken into account
in these methods. In this work, we propose an uncertaintyaware subsequence
based model which achieves a classification comparable to that of
state-of-the-art methods. Unlike conformal learning which estimates model
uncertainty on predictions, our method takes data uncertainty as additional
input. Moreover, our approach is explainable-by-design, giving domain experts
the ability to inspect the model and explain its predictions. The
explainability of the proposed method has also the potential to inspire new
developments in theoretical astrophysics modeling by suggesting important
subsequences which depict details of light curve shapes. The dataset, the
source code of our experiment, and the results are made available on a public
repository.
