Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unrealistic feature perturbations and ignore temporal and cross-feature dependencies, leading to unreliable attributions. We propose a conditional attribution framework that explains anomalies relative to contextually similar normal system states. Instead of using marginal or randomly sampled baselines, our method retrieves representative normal instances conditioned on the anomalous observation, enabling dependency-preserving and operationally meaningful explanations. To support high-dimensional time-series data, contextual retrieval is performed in learned low-dimensional representations using both variational autoencoder latent spaces and UMAP manifold embeddings. By grounding the retrieval process in the system's learned manifold, this strategy avoids out-of-distribution artifacts and ensures attribution fidelity while maintaining computational efficiency. We further introduce confidence-aware and temporal evaluation metrics for assessing explanation reliability and responsiveness. Experiments on the SWaT and MSDS benchmarks demonstrate that the proposed approach consistently improves root-cause identification accuracy, temporal localization, and robustness across multiple anomaly detection models. These results highlight the practical utility of conditional attribution for explainable anomaly diagnosis in complex time-series systems. Code and models are available at: https://github.com/dfki-av/Conditional-Attribution-for-Root-Cause-Analysis-in-Time-Series-Anomaly-Detection.

翻译：根因分析（RCA）对于确保复杂真实世界系统中时间序列异常检测的可靠运行至关重要。现有解释方法常依赖不切实际的特征扰动，且忽略时间依赖和跨特征依赖关系，导致归因不可靠。我们提出一种条件归因框架，通过关联与异常状态上下文相似的正态系统状态来解释异常。该方法摒弃边际基线或随机采样基线，而是基于异常观测值检索具有代表性的正态实例，从而生成保持依赖关系且具有操作意义的解释。为支持高维时间序列数据，我们利用变分自编码器潜在空间和UMAP流形嵌入，在学习到的低维表示中执行上下文检索。通过将检索过程锚定在系统学习到的流形中，该策略避免了分布外伪影，在保证计算效率的同时确保了归因保真度。我们进一步引入置信度感知和时间评估指标，用于衡量解释的可靠性与响应性。在SWaT和MSDS基准数据集上的实验表明，该方法在不同异常检测模型上均能持续提升根因识别准确率、时间定位精度和鲁棒性。这些结果凸显了条件归因在复杂时间序列系统中实现可解释异常诊断的实用价值。代码和模型已开源：https://github.com/dfki-av/Conditional-Attribution-for-Root-Cause-Analysis-in-Time-Series-Anomaly-Detection。