用于多变量时间序列精确异常检测的因果解耦学习
Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series
AI总结：
提出CDRL4AD方法，通过时间异构图和解耦潜在变量显式推断多变量时间序列不同时间段的因果关系，在准确率和根因分析上优于现有方法。
摘要：
解耦复杂的因果关系对精确检测异常很重要。在多变量时间序列分析中，数据变量随时间的动态交互使因果关系的解释复杂化。传统方法在无监督设定下假设变量间统计独立，而近期方法通过图表示学习捕捉特征相关性。然而，其表示未能显式推断不同时间段的因果关系。为解决此问题，我们提出用于异常检测的因果解耦表示学习（CDRL4AD），以检测异常并识别多变量时间序列中的因果关系。首先，我们将因果过程设计为模型输入、时间异构图和因果关系。其次，我们的表示识别不同时间段的因果关系，并解耦潜在变量以推断相应的因果因子。第三，我们在真实数据集上的实验表明，CDRL4AD在准确率和根因分析方面优于现有方法。第四，我们的模型分析验证了CDRL4AD的超参数敏感性和时间复杂度。最后，我们进行案例研究，展示该方法如何协助人类专家诊断异常根因。
英文摘要：
Disentangling complex causal relationships is important for accurate detection of anomalies. In multivariate time series analysis, dynamic interactions among data variables over time complicate the interpretation of causal relationships. Traditional approaches assume statistical independence between variables in unsupervised settings, whereas recent methods capture feature correlations through graph representation learning. However, their representations fail to explicitly infer the causal relationships over different time periods. To solve the problem, we propose Causally Disentangled Representation Learning for Anomaly Detection (CDRL4AD) to detect anomalies and identify their causal relationships in multivariate time series. First, we design the causal process as model input, the temporal heterogeneous graph, and causal relationships. Second, our representation identifies causal relationships over different time periods and disentangles latent variables to infer the corresponding causal factors. Third, our experiments on real-world datasets demonstrate that CDRL4AD outperforms state-of-the-art methods in terms of accuracy and root cause analysis. Fourth, our model analysis validates hyperparameter sensitivity and the time complexity of CDRL4AD. Last, we conduct a case study to show how our approach assists human experts in diagnosing the root causes of anomalies.
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。
