Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
OracleAD is an unsupervised framework for multivariate time series anomaly detection that models temporal dynamics by encoding each variable's past sequence into a causal embedding. It uses self-attention to capture spatial relationships and compares the current state against a Stable Latent Structure (SLS) to identify anomalies through a dual scoring mechanism, providing both prediction error and structural deviation scores.