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

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

URL: http://arxiv.org/abs/2407.00492v1
Date: Sat, 29 Jun 2024 16:49:28 GMT
Title: Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model
Authors: Xueying Long, Daniel F. Schmidt, Christoph Bergmeir, Slawek Smyl,
Abstract summary: A generalised exponential smoothing model was proposed that is able to capture strong trends and volatility in time series. The fitting procedure, which is based on the NUTS sampler, is very computationally expensive. We propose several modifications to the original model, as well as a bespoke Gibbs sampler for posterior exploration.
Score: 6.099774114286838
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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