  Generating high quality uncertainty estimates for sequential regression,
particularly deep recurrent networks, remains a challenging and open problem.
Existing approaches often make restrictive assumptions (such as stationarity)
yet still perform poorly in practice, particularly in presence of real world
non-stationary signals and drift. This paper describes a flexible method that
can generate symmetric and asymmetric uncertainty estimates, makes no
assumptions about stationarity, and outperforms competitive baselines on both
drift and non drift scenarios. This work helps make sequential regression more
effective and practical for use in real-world applications, and is a powerful
new addition to the modeling toolbox for sequential uncertainty quantification
in general.
