This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods.

The streaming model is applicable in many algorithmic and ML related tasks where the size ofthe data farexceeds the available storage.