用自编码器解码异常，让工业时序数据的离群点可解释。
Interpreting Outliers in Time Series Data through Decoding Autoencoder
- 用自编码器压缩时序数据，从潜在特征中检测异常。
- 提出融合多方法的解释集成新框架，提升解释表达力。
- 结合领域专家评估，量化验证解释质量，适合工业场景。
异常检测在多个领域至关重要，在制造等关键系统中，模型误判可能造成重大损失与安全风险。因此，部署黑箱模型时亟需可解释人工智能（XAI）。本研究聚焦德国汽车供应链的制造时序数据，采用自编码器对完整时序进行压缩，并在潜在空间中应用异常检测。为解释异常，首先将广泛使用的XAI技术应用于自编码器的编码器；其次，提出新型解释集成方法AEE（Aggregated Explanatory Ensemble），融合多种XAI技术的解释，生成更丰富、更一致的解释结果。为评估解释质量，第三，提出一种定量测量编码器解释质量的新方法，并通过领域专家进行定性评估。实验验证了所提方法在工业时序数据上的有效性与可解释性优势。
原文摘要 · Abstract (English)
Outlier detection is a crucial analytical tool in various fields. In critical systems like manufacturing, malfunctioning outlier detection can be costly and safety-critical. Therefore, there is a significant need for explainable artificial intelligence (XAI) when deploying opaque models in such environments. This study focuses on manufacturing time series data from a German automotive supply industry. We utilize autoencoders to compress the entire time series and then apply anomaly detection techniques to its latent features. For outlier interpretation, we (i) adopt widely used XAI techniques to the autoencoder's encoder. Additionally, (ii) we propose AEE, Aggregated Explanatory Ensemble, a novel approach that fuses explanations of multiple XAI techniques into a single, more expressive interpretation. For evaluation of explanations, (iii) we propose a technique to measure the quality of encoder explanations quantitatively. Furthermore, we qualitatively assess the effectiveness of outlier explanations with domain expertise.
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