Dynamic Causal Contrastive Learning for Multivariate Time-Series Anomaly Detection

Multivariate time-series anomaly detection requires modeling temporal patterns and inter-variable relationships varying across operating states. Using static causal graphs to guide contrastive augmentation can misalign downstream variable updates in positive samples and overlook changes in outgoing connectivity when selecting negative perturbation targets. We propose DCCAD, a dynamic causal contrastive learning framework that constructs positive and negative samples using the causal relationships estimated for each window. DCCAD learns a shared causal predictor from blocks of normal data and freezes it to estimate dynamic graphs under a consistent construction procedure for training and unseen sequences. Positive augmentation selects sources with nonzero current out-degrees when available and coordinates their perturbations with updates to graph-specified downstream variables. Negative augmentation prioritizes variables with larger current out-degrees and samples a perturbation ratio to vary coverage. These augmentations encourage representations that distinguish variations consistent with the estimated causal relationships from synthetic disruptions of those relationships. During inference, DCCAD combines representation distance and causal prediction error with a graph change score quantifying changes in estimated causal influence and topology relative to a historical reference. Experiments on five real-world datasets and one synthetic dynamical system demonstrate strong detection performance, with ablation studies validating the contribution of each component.