An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling

Results show that the increase in RBM free energy in the off-nominal conditions compared to that in the nominal conditions can be used for anomaly detection and the proposed framework formulates a strong learning model (STPN+RBM) from weak frequentist model–STPN, via boosting with RBM.