Anomaly Detection for Multivariate Time Series with Multi-scale Feature Interactions

Efficient anomaly detection in multivariate time series (MTS) data holds paramount importance in the realm of monitoring intricate systems. However, numerous recent methods tend to disregard the vital intra-variable and inter-variable dependencies across distinct temporal and variable scales, leading to inadequate anomaly detection outcomes. To tackle these challenges, we propose a Multi-scale Feature Interactions (MSFI), a novel unsupervised model for anomaly detection in MTS. The MSFI model leverages multi-scale feature interaction blocks, which exploit the variance of each timestamp vector within various scale windows to capture the interdependency among neighboring timestamps. This model also facilitates a comprehensive exploration of variable correlations. Moreover, we propose a normalization-based method for computing attention coefficients to enhance inter-variable correlations and evidence its superiority over the softmax-based method under certain conditions. The resulting dependency features are merged, and a transformer is employed for time series data reconstruction. Furthermore, we introduce three types of noise and incorporate a dynamic noise regulation mechanism during model training to enhance robustness. Extensive experimental evaluations validate the superior performance of our model compared to state-of-the-art approaches.