  Systems are commonly monitored for health and security through collection and
streaming of multivariate time series. Advances in time series forecasting due
to adoption of multilayer recurrent neural network architectures make it
possible to forecast in high-dimensional time series, and identify and classify
novelties early, based on subtle changes in the trends. However, mainstream
approaches to multi-variate time series predictions do not handle well cases
when the ongoing forecast must include uncertainty, nor they are robust to
missing data. We introduce a new architecture for time series monitoring based
on combination of state-of-the-art methods of forecasting in high-dimensional
time series with full probabilistic handling of uncertainty. We demonstrate
advantage of the architecture for time series forecasting and novelty
detection, in particular with partially missing data, and empirically evaluate
and compare the architecture to state-of-the-art approaches on a real-world
data set.
