C-SHAP: Concept-Based XAI for Time Series
A new explainable AI method called C-SHAP addresses the lack of high-level temporal explanations in time series analysis. Unlike existing techniques that focus on points or subsequences, C-SHAP uses concepts defined as high-level patterns extracted from the data. It leverages the SHAP method to measure each concept's influence on predictions. The approach targets high-stakes domains like healthcare and industry where model reliability is critical. The paper is available on arXiv under ID 2504.11159.
Key facts
- C-SHAP is a concept-based XAI approach for time series
- Concepts are high-level patterns extracted from time series data
- C-SHAP uses SHAP to determine concept influence on predictions
- Targets high-stakes domains like healthcare and industry
- Existing XAI techniques for time series are point- or subsequence-based
- Paper published on arXiv with ID 2504.11159
Entities
Institutions
- arXiv