DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series
  Anomaly Detection
Lixu Wang Shichao Xu Xinyu Du Qi Zhu
Anomaly detection in time-series data is crucial for identifying faults,
failures, threats, and outliers across a range of applications. Recently, deep
learning techniques have been applied to this topic, but they often struggle in
real-world scenarios that are complex and highly dynamic, e.g., the normal data
may consist of multiple distributions, and various types of anomalies may
differ from the normal data to different degrees. In this work, to tackle these
challenges, we propose Distribution-Augmented Contrastive Reconstruction
(DACR). DACR generates extra data disjoint from the normal data distribution to
compress the normal data's representation space, and enhances the feature
extractor through contrastive learning to better capture the intrinsic
semantics from time-series data. Furthermore, DACR employs an attention
mechanism to model the semantic dependencies among multivariate time-series
features, thereby achieving more robust reconstruction for anomaly detection.
Extensive experiments conducted on nine benchmark datasets in various anomaly
detection scenarios demonstrate the effectiveness of DACR in achieving new
state-of-the-art time-series anomaly detection.