  Since neural networks play an increasingly important role in critical
sectors, explaining network predictions has become a key research topic.
Counterfactual explanations can help to understand why classifier models decide
for particular class assignments and, moreover, how the respective input
samples would have to be modified such that the class prediction changes.
Previous approaches mainly focus on image and tabular data. In this work we
propose SPARCE, a generative adversarial network (GAN) architecture that
generates SPARse Counterfactual Explanations for multivariate time series. Our
approach provides a custom sparsity layer and regularizes the counterfactual
loss function in terms of similarity, sparsity, and smoothness of trajectories.
We evaluate our approach on real-world human motion datasets as well as a
synthetic time series interpretability benchmark. Although we make
significantly sparser modifications than other approaches, we achieve
comparable or better performance on all metrics. Moreover, we demonstrate that
our approach predominantly modifies salient time steps and features, leaving
non-salient inputs untouched.
