Exploiting Variable Correlation with Masked Modeling for Anomaly Detection in Time Series

Online anomaly detection in multi-variate time series is a challenging problem particularly when there is no supervision information. Autoregressive predictive models are often used for this task, but such detection methods often overlook correlations between variables observed in the most recent step and thus miss some anomalies that violate normal variable relations. In this work, we propose a masked modeling approach that captures variable relations and temporal relations in a single predictive model. Our method can be combined with a wide range of predictive models. Our experiment shows that our new masked modeling method improves detection performance over pure autoregressive models when the time series itself is not very predictable.