  Time series forecasting is an active research topic in academia as well as
industry. Although we see an increasing amount of adoptions of machine learning
methods in solving some of those forecasting challenges, statistical methods
remain powerful while dealing with low granularity data. This paper introduces
a refined Bayesian exponential smoothing model with the help of probabilistic
programming languages including Stan. Our model refinements include additional
global trend, transformation for multiplicative form, noise distribution and
choice of priors. A benchmark study is conducted on a rich set of time-series
data sets for our models along with other well-known time series models.
