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.
翻译:时间序列预测是学术界和工业界的一个积极研究课题。虽然我们看到在解决其中一些预测挑战方面越来越多地采用机器学习方法,但统计方法在处理低颗粒度数据时仍然很有力。本文介绍了一个精细的贝叶斯指数平滑模型,并借助包括斯坦在内的概率性编程语言。我们的模型改进包括额外的全球趋势、倍增形式的转变、噪音的传播和前科的选择。正在对我们模型和其他著名时间序列模型进行一套丰富的时间序列数据集和其他著名时间序列模型进行一项基准研究。