C-SHAP for time series: An approach to high-level temporal explanations
This work addresses the need for more interpretable AI in time-sensitive domains like energy forecasting, though it appears incremental by extending concept-based explanations to time series.
The paper tackles the problem of explaining AI model reasoning for time series by introducing C-SHAP, a concept-based method that captures high-level patterns, and demonstrates its effectiveness in an energy domain use case.
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.