  We introduce a new version of deep state-space models (DSSMs) that combines a
recurrent neural network with a state-space framework to forecast time series
data. The model estimates the observed series as functions of latent variables
that evolve non-linearly through time. Due to the complexity and non-linearity
inherent in DSSMs, previous works on DSSMs typically produced latent variables
that are very difficult to interpret. Our paper focus on producing
interpretable latent parameters with two key modifications. First, we simplify
the predictive decoder by restricting the response variables to be a linear
transformation of the latent variables plus some noise. Second, we utilize
shrinkage priors on the latent variables to reduce redundancy and improve
robustness. These changes make the latent variables much easier to understand
and allow us to interpret the resulting latent variables as random effects in a
linear mixed model. We show through two public benchmark datasets the resulting
model improves forecasting performances.
