Shapelet-Based Counterfactual Explanations for Multivariate Time Series

• URL: http://arxiv.org/abs/2208.10462v1
• Date: Mon, 22 Aug 2022 17:33:31 GMT
• Title: Shapelet-Based Counterfactual Explanations for Multivariate Time Series
• Authors: Omar Bahri, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi
• Abstract summary: We develop a model agnostic multivariate time series (MTS) counterfactual explanation algorithm. We test our approach on a real-life solar flare prediction dataset and prove that our approach produces high-quality counterfactuals. In addition to being visually interpretable, our explanations are superior in terms of proximity, sparsity, and plausibility.
• Score: 0.9990687944474738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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