GenFacts-Generative Counterfactual Explanations for Multi-Variate Time Series

• URL: http://arxiv.org/abs/2509.20936v2
• Date: Wed, 08 Oct 2025 11:16:15 GMT
• Title: GenFacts-Generative Counterfactual Explanations for Multi-Variate Time Series
• Authors: Sarah Seifi, Anass Ibrahimi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille,
• Abstract summary: We present textbfGenFacts, a novel generative framework for producing plausible and actionable counterfactual explanations.<n>We evaluate GenFacts on radar gesture recognition as an industrial use case and handwritten letter trajectories as an intuitive benchmark.
• Score: 5.364986293275663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Counterfactual explanations aim to enhance model transparency by illustrating how input modifications can change model predictions. In the multivariate time series domain, existing approaches often produce counterfactuals that lack validity, plausibility, or intuitive interpretability. We present \textbf{GenFacts}, a novel generative framework for producing plausible and actionable counterfactual explanations for time series classifiers. GenFacts introduces a structured approach to latent space modeling and targeted counterfactual synthesis. We evaluate GenFacts on radar gesture recognition as an industrial use case and handwritten letter trajectories as an intuitive benchmark. Across both datasets, GenFacts consistently outperforms baseline methods in plausibility metrics (+18.7\%) and achieves the highest interpretability scores in user studies. These results underscore that realism and user-centered interpretability, rather than sparsity alone, are vital for actionable counterfactuals in time series applications.

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