Counterfactual explanations provide an intuitive way to understand model decisions by identifying minimal changes required to alter an outcome. However, applying counterfactual methods to time series models remains challenging due to temporal dependencies, high dimensionality, and the lack of an intuitive human-interpretable representation. We introduce MASCOTS, a method that leverages the Bag-of-Receptive-Fields representation alongside symbolic transformations inspired by Symbolic Aggregate Approximation. By operating in a symbolic feature space, it enhances interpretability while preserving fidelity to the original data and model. Unlike existing approaches that either depend on model structure or autoencoder-based sampling, MASCOTS directly generates meaningful and diverse counterfactual observations in a model-agnostic manner, operating on both univariate and multivariate data. We evaluate MASCOTS on univariate and multivariate benchmark datasets, demonstrating comparable validity, proximity, and plausibility to state-of-the-art methods, while significantly improving interpretability and sparsity. Its symbolic nature allows for explanations that can be expressed visually, in natural language, or through semantic representations, making counterfactual reasoning more accessible and actionable.

翻译：反事实解释通过识别改变预测结果所需的最小变化，为理解模型决策提供了一种直观方法。然而，由于时间依赖性、高维特性以及缺乏直观的人类可解释表示，将反事实方法应用于时间序列模型仍具挑战性。本文提出MASCOTS方法，该方法结合感知野词袋表示与受符号聚合近似启发的符号变换技术。通过在符号特征空间中操作，该方法在保持对原始数据及模型忠实度的同时显著提升了可解释性。与现有依赖模型结构或基于自编码器采样的方法不同，MASCOTS以模型无关的方式直接生成具有意义且多样化的反事实观测，适用于单变量与多变量数据。我们在单变量和多变量基准数据集上评估MASCOTS，证明其在有效性、邻近性和合理性方面与最先进方法相当，同时显著提升了可解释性与稀疏性。其符号化特性支持通过可视化、自然语言或语义表征等多种形式呈现解释，使反事实推理更易于理解与实际应用。