  In Smyl et al. [Local and global trend Bayesian exponential smoothing models.
International Journal of Forecasting, 2024.], a generalised exponential
smoothing model was proposed that is able to capture strong trends and
volatility in time series. This method achieved state-of-the-art performance in
many forecasting tasks, but its fitting procedure, which is based on the NUTS
sampler, is very computationally expensive. In this work, we propose several
modifications to the original model, as well as a bespoke Gibbs sampler for
posterior exploration; these changes improve sampling time by an order of
magnitude, thus rendering the model much more practically relevant. The new
model, and sampler, are evaluated on the M3 dataset and are shown to be
competitive, or superior, in terms of accuracy to the original method, while
being substantially faster to run.
