时间序列分类中的假设解释：时间序列的反事实方法
What-If Explanations Over Time: Counterfactuals for Time Series Classification
AI总结：
本文综述了时间序列分类中反事实解释的最新算法，探讨了实例基于、模式驱动、梯度优化和生成模型等方法，分析了其挑战与局限，并提出了未来研究方向。
摘要：
反事实解释在可解释AI中 emerged as 一种强大的方法，提供what-if场景，揭示输入时间序列的最小变化如何改变模型预测。本文综述了最近针对时间序列分类的反事实解释算法。我们回顾了最先进的方法，涵盖实例基于的最近邻技术、模式驱动算法、基于梯度的优化和生成模型。对于每种方法，我们讨论了其底层方法、所针对的模型和分类器以及评估的 datasets。我们强调了在生成时间数据反事实时的独特挑战，如保持时间一致性、合理性及可操作的可解释性，这与表格或图像领域不同。我们分析了现有方法的优缺点，并在有效性、接近性、稀疏性、合理性等关键维度上比较了其效果。此外，我们实现了一个开源实现库，Counterfactual Explanations for Time Series (CFTS)，作为参考框架，包含许多算法和评估指标。我们讨论了该库在标准化评估和促进可解释时间序列技术实际应用中的贡献。最后，基于文献和识别的差距，我们提出了未来研究方向，包括改进以用户为中心的设计、整合领域知识以及时间序列预测中的反事实方法。
英文摘要：
Counterfactual explanations emerge as a powerful approach in explainable AI, providing what-if scenarios that reveal how minimal changes to an input time series can alter the model's prediction. This work presents a survey of recent algorithms for counterfactual explanations for time series classification. We review state-of-the-art methods, spanning instance-based nearest-neighbor techniques, pattern-driven algorithms, gradient-based optimization, and generative models. For each, we discuss the underlying methodology, the models and classifiers they target, and the datasets on which they are evaluated. We highlight unique challenges in generating counterfactuals for temporal data, such as maintaining temporal coherence, plausibility, and actionable interpretability, which distinguish the temporal from tabular or image domains. We analyze the strengths and limitations of existing approaches and compare their effectiveness along key dimensions (validity, proximity, sparsity, plausibility, etc.). In addition, we implemented an open-source implementation library, Counterfactual Explanations for Time Series (CFTS), as a reference framework that includes many algorithms and evaluation metrics. We discuss this library's contributions in standardizing evaluation and enabling practical adoption of explainable time series techniques. Finally, based on the literature and identified gaps, we propose future research directions, including improved user-centered design, integration of domain knowledge, and counterfactuals for time series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
