基于生成式对抗网络的自监督多元时间序列异常检测方法

周业瀚, 沈子钰, 周清, 李云

Self⁃supervised multivariate time series anomaly detection based on GAN

Yehan Zhou, Ziyu Shen, Qing Zhou, Yun Li

表2 CPCGAN与其他五种对比方法异常点检测的评价指标情况

Table 2 Performanceof CPCGAN and five baseline approaches on anomalous points detection

SWaT WADI SMD SMAP MSL P R F1 P R F1 P R F1 P R F1 P R F1 CPCGAN 0.9815 0.661 0.7899 0.991 0.1316 0.2323 0.9511 0.9484 0.9497 0.7581 0.9822 0.8557 0.882 0.9686 0.9232 AE 0.9324 0.5734 0.7101 0.3074 0.179 0.2262 0.5684 0.7894 0.6609 0.5633 0.6223 0.5915 0.571 0.6641 0.614 MAD⁃GAN 0.9585 0.6166 0.7504 0.9842 0.1351 0.2375 0.8722 0.8075 0.8386 0.7106 0.9521 0.8138 0.8457 0.9546 0.8968 LSTM⁃VAE 0.9655 0.6218 0.7564 0.9845 0.1334 0.2349 0.8592 0.8012 0.8291 0.7056 0.9752 0.8187 0.8601 0.9663 0.9101 DAGMM 0.4576 0.671 0.5441 0.0851 0.9117 0.1556 0.6573 0.8549 0.7431 0.6234 0.9776 0.7613 0.7467 0.9817 0.8482 TadGAN 0.9525 0.6481 0.7713 0.9561 0.1246 0.2204 0.9141 0.9362 0.925 0.7413 0.9867 0.8465 0.9052 0.8932 0.8991