基于自编码器的时间序列异常检测的反事实解释
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
AI总结：
针对自编码器时间序列异常检测不透明的问题，本文结合特征选择与基于梯度的反事实解释，在 SKAB 和工业数据集上验证其可产生更稀疏且有效的决策解释。
摘要：
现代机电系统的复杂性要求开发先进的诊断方法，例如能够检测偏差的异常检测。传统异常检测方法（如信号处理和统计建模）往往难以有效处理复杂系统的错综问题，尤其是在处理多变量信号时。相比之下，基于神经网络的异常检测方法，特别是自编码器（Auto-Encoder），已成为一种引人注目的替代方案，并展现出显著性能。然而，自编码器在其决策过程中表现出固有的不透明性，阻碍了其大规模实际部署。解决这种不透明性对于提升异常检测模型的可解释性和可信度至关重要。在本研究中，我们通过采用特征选择器来选择特征，并利用反事实解释为模型输出提供背景，从而应对这一挑战。我们在 SKAB 基准数据集和一个工业时间序列数据集上测试了该方法。基于梯度的反事实解释方法通过有效性、稀疏性和距离度量进行了评估。实验结果表明，我们提出的反事实方法能够通过解释比传统方法更少的信号，对模型决策过程提供有意义且有价值的洞察。这些洞察增强了异常检测模型的可信度和可解释性。
英文摘要：
The complexity of modern electro-mechanical systems require the development of sophisticated diagnostic methods like anomaly detection capable of detecting deviations. Conventional anomaly detection approaches like signal processing and statistical modelling often struggle to effectively handle the intricacies of complex systems, particularly when dealing with multi-variate signals. In contrast, neural network-based anomaly detection methods, especially Auto-Encoders, have emerged as a compelling alternative, demonstrating remarkable performance. However, Auto-Encoders exhibit inherent opaqueness in their decision-making processes, hindering their practical implementation at scale. Addressing this opacity is essential for enhancing the interpretability and trustworthiness of anomaly detection models. In this work, we address this challenge by employing a feature selector to select features and counterfactual explanations to give a context to the model output. We tested this approach on the SKAB benchmark dataset and an industrial time-series dataset. The gradient based counterfactual explanation approach was evaluated via validity, sparsity and distance measures. Our experimental findings illustrate that our proposed counterfactual approach can offer meaningful and valuable insights into the model decision-making process, by explaining fewer signals compared to conventional approaches. These insights enhance the trustworthiness and interpretability of anomaly detection models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
