Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection
CARMA proposes a reconstruction-based autoencoder using two latent spaces: a temporal one for sequence dynamics and a correlation one for inter-channel relationships. This explicitly models dependencies between channels, allowing it to detect anomalies that disrupt how channels interact, significantly outperforming existing methods.