<scp>SUE</scp> ‐ <scp>TS</scp> : A Surrogate Model Based Universal Explanation Framework for Time Series Forecasting

ABSTRACT Deep learning models have achieved significant success in time series forecasting, substantially improving predictive accuracy across several applications. The interpretability of these intricate models continues to pose a significant problem, especially in high‐stakes fields where transparency and trust are essential. Current explanation strategies are often limited to static assessments, lacking consistency and semantic significance into model behaviour. This article introduces SUE‐TS (Surrogate‐based Universal Explanation for Time Series), a versatile interpretability framework that develops a simple and interpretable surrogate to replicate the predictive behaviour of opaque forecasting models, therefore addressing existing restrictions. SUE‐TS incorporates a SHAP‐based closed‐loop feedback mechanism that enhances prediction accuracy and semantic consistency within the explanation domain. The methodology guarantees fidelity via dual‐consistency evaluation: predictive consistency, which evaluates numerical concordance between surrogate and black‐box models, and explanation‐space consistency, which confirms semantic coherence through high‐dimensional representation analysis. Comprehensive studies on seven benchmark time series datasets and six advanced forecasting systems reveal that SUE‐TS consistently attains high approximation fidelity, interpretability, and robustness. The surrogate models reproduce output behaviours and maintain the reasoning logic of the original models, even in complex temporal dynamics. SUE‐TS offers a scalable, model‐agnostic solution for reliable time series forecasting by reconciling performance with explainability. It provides a significant resource for enhancing interpretability in actual AI applications, facilitating downstream activities such as model audits, knowledge distillation, and decision‐support system integration.