Bayesian networks that model time series or sequences are known as dynamic Bayesian networks or DBNs.
They allow us to model either continuous time series variables or discrete sequence variables or both in the same model.
We can also mix time series and non time series variables in the same model, and use latent time series variables.
Once we have trained a time-series model from data, we can use it to perform prediction a single variable or the joint distribution of multiple
time series variables, or we can fill in past or current values.
We can also perform anomaly detection using a time series model, evaluating either real-time or batch data.