  Deep learning has shown impressive results in a variety of time series
forecasting tasks, where modeling the conditional distribution of the future
given the past is the essence. However, when this conditional distribution is
non-stationary, it poses challenges for these models to learn consistently and
to predict accurately. In this work, we propose a new method to model
non-stationary conditional distributions over time by clearly decoupling
stationary conditional distribution modeling from non-stationary dynamics
modeling. Our method is based on a Bayesian dynamic model that can adapt to
conditional distribution changes and a deep conditional distribution model that
handles multivariate time series using a factorized output space. Our
experimental results on synthetic and real-world datasets show that our model
can adapt to non-stationary time series better than state-of-the-art deep
learning solutions.
