  A large collection of time series poses significant challenges for classical
and neural forecasting approaches. Classical time series models fail to fit
data well and to scale to large problems, but succeed at providing uncertainty
estimates. The converse is true for deep neural networks. In this paper, we
propose a hybrid model that incorporates the benefits of both approaches. Our
new method is data-driven and scalable via a latent, global, deep component. It
also handles uncertainty through a local classical Gaussian Process model. Our
experiments demonstrate that our method obtains higher accuracy than
state-of-the-art methods.
