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.

翻译：在Smyl等人[局部与全局趋势贝叶斯指数平滑模型。《国际预测期刊》，2024年]的工作中，提出了一种广义指数平滑模型，该模型能够捕捉时间序列中的强劲趋势与波动性。该方法在许多预测任务中取得了最先进的性能，但其基于NUTS采样器的拟合过程计算成本极高。在本研究中，我们对原始模型提出了若干改进，并设计了一种专用的吉布斯采样器进行后验探索；这些改进将采样时间缩短了一个数量级，从而显著提升了模型的实用价值。新模型与采样器在M3数据集上进行了评估，结果表明其在精度方面与原方法相当或更优，同时运行速度大幅提升。