Deep learning models have 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 can handle large multivariate time series using a factorized output
space. Our experimental results on synthetic and popular public datasets show
that our model can adapt to non-stationary time series better than
state-of-the-art deep learning solutions