提出新方法解析多变量时间序列的复杂交互关系，更准更快。
GroupSegment-SHAP: Shapley Value Explanations with Group-Segment Players for Multivariate Time Series
- 将变量与时间区间结合形成分组段解释单元
- 在多个领域上解释力提升1.7倍，速度提高40%
- 适合金融、医疗等领域需理解多变量时序交互的研究者
多变量时间序列模型在医疗、工业、能源和金融领域表现优异，但其如何融合跨变量交互与时间动态仍不清晰。现有基于SHAP的方法通常独立处理特征与时间轴，割裂了多变量在特定时间段内共同形成的结构信号。本文提出GroupSegment SHAP（GS-SHAP），依据变量间依赖关系与时间分布变化，构建分组段解释单元，并通过Shapley值量化其贡献。在人体活动识别、电力系统预测、医疗信号分析和金融时间序列四个真实场景中评估，相比KernelSHAP、TimeSHAP、SequenceSHAP、WindowSHAP和TSHAP，GS-SHAP平均提升删除法忠实度（DeltaAUC）约1.7倍，且在相同扰动预算下平均运行时间减少约40%。金融案例显示，该方法能有效识别高波动时期关键市场变量间的可解释多变量-时序交互。
原文摘要 · Abstract (English)
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.
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