In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty Estimation), building on the recently proposed Graph Deviation Network (GDN). GLUE not only automatically learns complex dependencies between variables and uses them to better identify anomalous behavior, but also quantifies its predictive uncertainty, allowing us to account for the variation in the data as well to have more interpretable anomaly detection thresholds. Results on two real world datasets tell us that optimizing the negative Gaussian log likelihood is reasonable because GLUE's forecasting results are at par with GDN and in fact better than the vector autoregressor baseline, which is significant given that GDN directly optimizes the MSE loss. In summary, our experiments demonstrate that GLUE is competitive with GDN at anomaly detection, with the added benefit of uncertainty estimations. We also show that GLUE learns meaningful sensor embeddings which clusters similar sensors together.
翻译:在这项工作中,我们建议GLUE(带有本地不确定性估算的格子偏移网络),以最近提议的图形偏移网络为基础。GLUE不仅自动地学习变量之间的复杂依赖关系,并利用这些变量更好地识别异常行为,而且还量化其预测不确定性,使我们既能对数据的变化进行核算,也能有更多的可解释的异常检测阈值。两个真实世界数据集的结果告诉我们,优化负高斯日志的可能性是合理的,因为GLUE的预测结果与GDN相当,事实上比矢量自动反射基线要好,这一点很重要,因为GDN直接优化了MSE损失。简言之,我们的实验表明,GLUE在异常检测时与GDN具有竞争力,并增加了不确定性估计的好处。我们还表明,GLUE学会了将类似传感器组合在一起的有意义的传感器嵌入。