A weakly supervised time series anomaly detection method with dual-association discrepancy

Time series anomaly detection is a task of significant importance and has been widely employed in realistic scenarios. Most of existing methods conduct time series anomaly detection in an unsupervised manner, ignoring the limited number of labeled anomalies that are commonly available in practical situations. However, how to take advantage of these limited but valuable labeled anomalies to benefit time series anomaly detection requires fully exploration. To this end, we propose a weakly supervised time series anomaly detection method with dual-association discrepancy to effectively identify anomalies. Specifically, the proposed method utilizes the limited number of anomalies to enlarge the distance between the association discrepancy of the anomaly and that of the normal one, enforcing significant differences between abnormal and normal samples in discriminant places. We also design a masking strategy to enrich the representations of anomalies in latent feature space. Experiments with public available datasets have demonstrated the effectiveness of the proposed method.