Explainable Artificial Intelligence (XAI) has gained significant attention recently as the demand for transparency and interpretability of machine learning models has increased. In particular, XAI for time series data has become increasingly important in finance, healthcare, and climate science. However, evaluating the quality of explanations, such as attributions provided by XAI techniques, remains challenging. This paper provides an in-depth analysis of using perturbations to evaluate attributions extracted from time series models. A perturbation analysis involves systematically modifying the input data and evaluating the impact on the attributions generated by the XAI method. We apply this approach to several state-of-the-art XAI techniques and evaluate their performance on three time series classification datasets. Our results demonstrate that the perturbation analysis approach can effectively evaluate the quality of attributions and provide insights into the strengths and limitations of XAI techniques. Such an approach can guide the selection of XAI methods for time series data, e.g., focusing on return time rather than precision, and facilitate the development of more reliable and interpretable machine learning models for time series analysis.

翻译：可解释人工智能（XAI）因机器学习模型对透明度和可解释性需求的增加而近年来备受关注。尤其在金融、医疗和气候科学领域，针对时间序列数据的XAI变得日益重要。然而，评估XAI技术所提供解释（如归因）的质量仍是一项挑战。本文深入分析了如何利用扰动来评估从时间序列模型中提取的归因。扰动分析涉及系统性地修改输入数据，并评估其对XAI方法生成的归因所产生的影响。我们将该方法应用于多种最先进的XAI技术，并在三个时间序列分类数据集上评估其性能。结果表明，扰动分析方法能够有效评估归因的质量，并揭示XAI技术的优势与局限。此类方法可为时间序列数据选择XAI方法提供指导（例如侧重返回时间而非精确度），并促进更可靠、可解释的时间序列分析机器学习模型的发展。