Time series are ubiquitous in domains such as energy forecasting, healthcare, and industry. Using AI systems, some tasks within these domains can be efficiently handled. Explainable AI (XAI) aims to increase the reliability of AI solutions by explaining model reasoning. For time series, many XAI methods provide point- or sequence-based attribution maps. These methods explain model reasoning in terms of low-level patterns. However, they do not capture high-level patterns that may also influence model reasoning. We propose a concept-based method to provide explanations in terms of these high-level patterns. In this paper, we present C-SHAP for time series, an approach which determines the contribution of concepts to a model outcome. We provide a general definition of C-SHAP and present an example implementation using time series decomposition. Additionally, we demonstrate the effectiveness of the methodology through a use case from the energy domain.

本文研究了解释性人工智能(XAI)在时间序列分析中的应用，针对传统方法无法捕捉高层次模式的问题，提出了C-SHAP方法，使用概念化方法提供模型输出的高层次解释。研究结果显示，该方法在能源领域的案例中有效提升了解释的可靠性。

C-SHAP时间序列：高层次时间解释的方法