基于图自编码器的无监督多变量时间序列异常检测

针对多变量时间序列复杂的时间相关性和高维度使得异常检测性能较差的问题,以对抗训练框架为基础提出基于图自编码的无监督多变量时间序列异常检测模型.首先,将特征转换为嵌入向量来表示;其次,将划分好的时间序列结合嵌入向量转换为图结构数据;然后,用两个图自编码器模拟对抗训练重构数据样本;最后,根据测试数据在模型训练下的重构误差进行异常判定.将提出的方法与5种基线异常检测方法进行比较.实验结果表明,提出的模型在测试数据集获得了最高的F1分数,总体性能分F1分数比最新的异常检测模型USAD提高了28.4%.可见提出的模型有效提高异常检测性能.

GAE-based Unsupervised Anomaly Detection of Multivariable Time Series

The complex time correlation and high dimension of multivariable time series lead to poor anomaly detection performance. In view of this, an unsupervised anomaly detection model of multivariable time series based on graph autoencoders (GAEs) is proposed with the adversarial training framework as the basis. First, features are converted into embedded vectors. Secondly, the divided time series and embedded vectors are converted into graph-structured data. Then, two GAEs are used to simulate the adversarial training and reconstruct data samples. Finally, the anomaly is determined according to the reconstruction error of the test data under the model training. The proposed method is compared with five baseline anomaly detection methods. The experimental results show that the proposed model achieves the highest F1 score on the test data set, and the overall performance F1 score is 28.4% higher than that of the latest anomaly detection model, namely, USAD. Therefore, it can be seen that the proposed model can effectively improve the performance of anomaly detection.