NVExplain：基于潜在轨迹分析与结构保持替代模型的时间序列预测解释方法
NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates
- NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出模型无关的NVExplain框架，通过潜在轨迹分析与结构保持替代模型，提升时间序列预测解释的忠实性与效率，解释具鲁棒性。
AI中文摘要：
时间序列预测模型被广泛应用于高风险场景，但其预测结果仍难以解释，因为现有事后解释方法常忽略时间依赖性，且无法提供针对预测步长的解释。本文提出一种模型无关的可解释性框架，通过将每个预测步长归因于时间相关的历史滞后项来解释预测结果。该框架将预测建模为潜在轨迹，并引入语义流以量化模型内部表示中信息随时间的演化方式；通过聚合语义流，构建捕捉步长解析时间影响的滞后-步长归因矩阵。为提升可解释性，本文进一步生成结构保持扰动并拟合稀疏局部替代模型，生成人类可读且时间一致的解释。本文在多个基准数据集上使用忠实性和稳定性诊断方法评估该方法，结果显示，语义流变体与标准事后基线相比具有相当或更优的忠实性，同时计算效率显著更高；稳定性分析进一步表明，生成的解释具有鲁棒性，并确定了需谨慎应用解释的场景。
英文摘要：
Time series forecasting models are widely used in high-stakes settings, yet their predictions remain difficult to interpret because existing post-hoc methods often ignore temporal dependence and fail to provide horizon-specific explanations. We propose a model-agnostic explainability framework that explains forecasting predictions by attributing each forecast horizon to temporally relevant historical lags. The framework models forecasting as a latent trajectory and introduces semantic flow to quantify how information evolves across time in the model's internal representations. By aggregating semantic flow, it constructs a lag-horizon attribution matrix that captures horizon-resolved temporal influence. To improve explainability, we further generate structure-preserving perturbations and fit sparse local surrogate models, producing human-readable and temporally coherent explanations. We evaluate the method using faithfulness and stability diagnostics across multiple benchmark datasets. Results show that the semantic-flow variant achieves competitive or superior faithfulness compared to standard post-hoc baselines, while being substantially more computationally efficient. Stability analysis further demonstrates that the explanations are robust and identifies regimes where interpretation should be applied with caution.
