  Detecting anomalies for multivariate time-series without manual supervision
continues a challenging problem due to the increased scale of dimensions and
complexity of today's IT monitoring systems. Recent progress of unsupervised
time-series anomaly detection mainly use deep autoencoders to solve this
problem, i.e. training on normal samples and producing significant
reconstruction error on abnormal inputs. However, in practice, autoencoders can
reconstruct anomalies so well, due to powerful capabilites of neural networks.
Besides, these approaches can be ineffective for identifying non-point
anomalies, e.g. contextual anomalies and collective anomalies, since they
solely utilze a point-wise reconstruction objective. To tackle the above
issues, we propose MemAAE (\textit{Memory-augmented Adversarial Autoencoders
with Deep Reconstruction and Prediction}), a novel unsupervised anomaly
detection method for time-series. By jointly training two complementary proxy
tasks, reconstruction and prediction, with a shared network architecture, we
show that detecting anomalies via multiple tasks obtains superior performance
rather than single-task training. Additionally, a compressive memory module is
introduced to preserve normal patterns, avoiding unexpected generalization on
abnormal inputs. Through extensive experiments, MemAAE achieves an overall F1
score of 0.90 on four public datasets, significantly outperforming the best
baseline by 0.02.
