  We develop a supervised machine learning model that detects anomalies in
systems in real time. Our model processes unbounded streams of data into time
series which then form the basis of a low-latency anomaly detection model.
Moreover, we extend our preliminary goal of just anomaly detection to
simultaneous anomaly prediction. We approach this very challenging problem by
developing a Bayesian Network framework that captures the information about the
parameters of the lagged regressors calibrated in the first part of our
approach and use this structure to learn local conditional probability
distributions.
