发表机构
XITASO GmbH（西塔索有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究时间序列模型可解释性方法中基于边际影响的归因方法失效问题，指出主要因计算不匹配，定义DAG忠实性，发现标准归因方法如SHAP通常不满足DAG忠实性，其扩展也有同样计算限制。
AI 中文摘要
时间序列模型的可解释性方法主要产生扁平的归因分数：通过标量量化时间戳处特征的直接影响。我们证明，此类方法的主要失败模式不是标量格式本身，而是一种基本的计算不匹配：现有方法通过边际条件或流形外梯度计算分数，这两种方法在自相关下将直接时间依赖性与介导的依赖性混为一谈。我们还定义了DAG忠实性：如果它编码的时间依赖性图与模型隐式学习的时间有向无环图（DAG）马尔可夫等价，则解释是DAG忠实的。特别是，我们观察到标准归因方法，特别是SHAP，通常不是DAG忠实的，并且最近的时间序列感知扩展继承了相同的计算限制。
英文摘要
Explainability methods for time series models predominantly produce flat attribution scores: they quantify the direct influence of a feature at a timestamp by a scalar. We prove that the dominant failure mode of such methods is not the scalar format itself but a fundamental computational mismatch: existing methods compute scores via marginal conditioning or off-manifold gradients, both of which conflate direct temporal dependencies with mediated ones under autocorrelation. We also define DAG-faithfulness: an explanation is DAG-faithful if the temporal dependency graph it encodes is Markov-equivalent to the temporal directed acyclic graph (DAG) implicitly learned by the model. Particularly, we observe that standard attribution methods, specifically SHAP, are not DAG-faithful in general, and that recent time-series-aware extensions inherit the same computational limitation.
CommentsAccepted at the Workshop on Explainable Artificial Intelligence (XAI), International Joint Conference on Artificial Intelligence (IJCAI 2026)
