Multivariate time-series models achieve strong predictive performance in healthcare, industry, energy, and finance, but how they combine cross-variable interactions with temporal dynamics remains unclear. SHapley Additive exPlanations (SHAP) are widely used for interpretation. However, existing time-series variants typically treat the feature and time axes independently, fragmenting structural signals formed jointly by multiple variables over specific intervals. We propose GroupSegment SHAP (GS-SHAP), which constructs explanatory units as group-segment players based on cross-variable dependence and distribution shifts over time, and then quantifies each unit's contribution via Shapley attribution. We evaluate GS-SHAP across four real-world domains: human activity recognition, power-system forecasting, medical signal analysis, and financial time series, and compare it with KernelSHAP, TimeSHAP, SequenceSHAP, WindowSHAP, and TSHAP. GS-SHAP improves deletion-based faithfulness (DeltaAUC) by about 1.7x on average over time-series SHAP baselines, while reducing wall-clock runtime by about 40 percent on average under matched perturbation budgets. A financial case study shows that GS-SHAP identifies interpretable multivariate-temporal interactions among key market variables during high-volatility regimes.

翻译：多变量时间序列模型在医疗健康、工业、能源及金融领域展现出强大的预测性能，但其如何将跨变量交互与时间动态相结合仍不明确。沙普利可加解释（SHAP）被广泛用于模型解释。然而，现有时间序列变体通常独立处理特征轴与时间轴，导致由多个变量在特定区间内共同形成的结构性信号被割裂。本文提出GroupSegment SHAP（GS-SHAP），该方法基于跨变量依赖关系与时变分布漂移构建以组段为单位的解释参与者，进而通过沙普利归因量化各单元的贡献。我们在四个现实领域评估GS-SHAP：人体活动识别、电力系统预测、医疗信号分析与金融时间序列，并与KernelSHAP、TimeSHAP、SequenceSHAP、WindowSHAP及TSHAP进行对比。在相同扰动预算下，GS-SHAP相较于时间序列SHAP基线方法，将基于删除的忠实度指标（ΔAUC）平均提升约1.7倍，同时将实际运行时间平均降低约40%。一项金融案例研究表明，GS-SHAP能够识别高波动区间内关键市场变量间可解释的多变量-时间交互模式。