Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model

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 proce dure, which is based on the NUTS sampler, is very computationally expensive. In this work, w e propose several modifications to the original model, as well as a bespoke Gibbs sampler for p osterior 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 evalu ated 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.