通过分析模型内部时序演化，为时间序列预测提供分时程的可解释性。
NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates
- 将预测建模为潜在轨迹，用语义流量化信息随时间演变
- 生成结构保持扰动，构建可读且时序一致的解释
- 在多个数据集上验证，解释更忠实且计算效率更高
时间序列预测模型广泛应用于高风险场景，但其预测结果难以解释，因现有事后方法常忽略时序依赖性，且无法提供分时程解释。本文提出一种模型无关的可解释性框架，通过将每个预测时程归因于相关的历史滞后项来解释预测。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征中信息随时间的演化过程。通过聚合语义流，构建捕捉时程解析时序影响的滞后-时程归因矩阵。为进一步提升可解释性，我们生成结构保持扰动并拟合稀疏局部代理模型，从而产生人类可读且时序连贯的解释。我们在多个基准数据集上使用忠实性与稳定性诊断评估该方法。结果表明，语义流变体在忠实性方面达到或优于标准事后基线，同时计算效率显著提升。稳定性分析进一步显示解释具有鲁棒性，并识别出需谨慎应用解释的特定场景。
原文摘要 · Abstract (English)
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.
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