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Predictive monitoring – making predictions about future states and monitoring if the predicted states satisfy requirements – offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherentarXiv:2011.00384v3 fatcat:gxufwlzxnbgqvja7wwtkqtey5i