Explainable Time-Series Forecasting: A Sampling-Free SHAP Approach for Transformers
Published:Dec 23, 2025 17:02
•1 min read
•ArXiv
Analysis
This research explores enhancing the interpretability of time-series forecasting models using SHAP values, a well-established method for explaining machine learning model predictions. The utilization of a sampling-free approach suggests potential improvements in computational efficiency and practical applicability within the context of Transformers.
Key Takeaways
Reference
“The article focuses on explainable time-series forecasting using a sampling-free SHAP approach for Transformers.”