  As machine learning and deep learning models have become highly prevalent in
a multitude of domains, the main reservation in their adoption for
decision-making processes is their black-box nature. The Explainable Artificial
Intelligence (XAI) paradigm has gained a lot of momentum lately due to its
ability to reduce models opacity. XAI methods have not only increased
stakeholders' trust in the decision process but also helped developers ensure
its fairness. Recent efforts have been invested in creating transparent models
and post-hoc explanations. However, fewer methods have been developed for time
series data, and even less when it comes to multivariate datasets. In this
work, we take advantage of the inherent interpretability of shapelets to
develop a model agnostic multivariate time series (MTS) counterfactual
explanation algorithm. Counterfactuals can have a tremendous impact on making
black-box models explainable by indicating what changes have to be performed on
the input to change the final decision. We test our approach on a real-life
solar flare prediction dataset and prove that our approach produces
high-quality counterfactuals. Moreover, a comparison to the only MTS
counterfactual generation algorithm shows that, in addition to being visually
interpretable, our explanations are superior in terms of proximity, sparsity,
and plausibility.
