ACTS: Adaptive Counterfactual Explanations for Time Series Data Using Barycenters

EXplainable Artificial Intelligence (XAI) methods are essential for understanding complex machine learning models. This paper presents Adaptive Counterfactual Explanations for Time Series (ACTS), a model-agnostic algorithm that improves the interpretability, sparsity, and contiguity of counterfactuals. ACTS leverages Barycenter Averaging (DBA) with Dynamic Time Warping (DTW) and a regularized loss function combining prediction, DBA, L1 regularization, sparsity, and smoothness terms. Empirical results on real-world datasets show ACTS generates high-quality explanations with fewer outliers and better interpretability than current methods, advancing XAI in time series analysis.