Efficient XAI in multivariate time series: Perturbation-based dynamic windows with concept and global explanations

— This study proposes a scalable explainable artificial intelligence (XAI) framework for multivariate time-series classification by addressing the computational limitations of SHAP-based temporal attribution methods. While approaches such as Temporal SHAP , TimeSHAP , and Dynamic WindowSHAP provide window-level interpretability, their combinatorial complexity grows rapidly with sequence length and feature dimensionality, limiting practical applicability. To overcome these limitations, we introduce a perturbation-driven dynamic window framework that estimates temporal importance by directly measuring model output deviations under structured temporal perturbations, thereby avoiding explicit Shapley-value enumeration. This approximation enables efficient temporal attribution while preserving dominant importance patterns observed in SHAP-based methods. The proposed unified framework consists of four components: (1) Dynamic WindowPerturbation for efficient window-level attribution, (2) PCA-integrated dynamic window analysis to reduce feature redundancy while reconstructing attributions in the original feature space, (3) concept-level temporal attribution based on time-series decomposition, and (4) global temporal aggregation for dataset-level interpretability. Experiments on a synthetic MIMIC-III clinical dataset and a real Swarm Behaviour dataset using GRU and LSTM models demonstrate up to 89% reduction in explanation runtime compared to SHAP-based methods and up to 9 × acceleration through PCA integration. Independent faithfulness evaluation using deletion and insertion metrics confirms that the perturbation-based approach preserves decision-relevant temporal importance structures despite relaxing Shapley axioms. Overall, the results demonstrate that dominant temporal importance patterns in high-dimensional sequential models can be recovered without exact Shapley-value computation, enabling a practical trade-off between axiomatic guarantees and computational scalability.