Counterfactual explanations are essential for inter-preting predictions made by time series classifiers, yet existing methods often disrupt inherent temporal dependencies, resulting in unrealistic explanations. We propose CACTUS (Cross-Aligned Counterfactuals for Time Series Classification), a novel model-agnostic framework designed to generate interpretable explanations for black-box models. CACTUS leverages Dynamic Time Warping (DTW) Barycenter Averaging and introduces a cross-correlation loss to enforce temporal consistency and alignment. We introduce two variants of CACTUS: one that emphasizes minimal perturbations while maintaining classification validity, and another that ensures gradual, realistic modifications in the time series. Comprehensive experiments on diverse benchmark datasets demonstrate that CACTUS outperforms state-of-the-art optimization-based methods by generating more reliable and interpretable counterfactual explanations, establishing it as a robust tool for post-hoc time series models analysis and explanations.

Understanding the predictions of time series classifiers is essential in high-stakes domains such as health-care, finance, and security. Counterfactual explanations, which identify minimal perturbations to input sequences that alter model predictions, provide intuitive and actionable insights. However, existing methods often struggle to generate realistic or diverse counterfactuals, especially for sequential data with strong temporal dependencies. In this work, we propose DiverseLCF, a post-hoc framework for generating diverse and plausible counterfactual explanations for time series classifiers. By optimizing in the latent space of a pretrained variational autoencoder (VAE), DiverseLCF ensures that generated counterfactuals remain temporally coherent and aligned with the data manifold. We formulate the generation process as a multi-objective optimization problem that balances validity, proximity, and diversity. Extensive experiments show that DiverseLCF consistently generates valid, plausible, and diverse coun-terfactuals with minimal proximity trade-offs, providing a robust and generalizable tool for interpreting time series models.

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.

Artificial intelligence (AI) and algorithmic decision-making are profoundly shaping various aspects of society, with applications in healthcare, business, education, etc. As these systems become more integral to high-stakes decisions, concerns about their transparency and interpretability are growing. To address these concerns, explainable AI (XAI) methods have been developed, with counterfactual explanations emerging as a powerful tool. Counterfactuals help users understand AI decisions by demonstrating how small changes in input could alter the outcome, providing a clear and intuitive way to interpret AI behavior. Despite their potential, generating valid, interpretable, and efficient counterfactual explanations is particularly challenging in time series domains, where data points are interdependent. In this paper, we introduce a novel approach to counterfactual explanations guided by ShapeDTW Barycenter Averaging (ShapeDBA). By integrating ShapeDBA into the counterfactual generation process, we ensure that the produced explanations are not only valid and interpretable but also efficient to generate. Our approach provides counterfactuals that align closely with human intuition while maintaining the computational efficiency required for practical deployment. This work represents a significant step forward in the development of interpretable AI systems, particularly in the complex domain of time series analysis.