This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models. Their development is motivated by fast-growing, volatile time series, and facilitated by state-of-the-art Bayesian fitting techniques. When applied to the M3 competition data set, they outperform the best algorithms in the competition as well as other benchmarks, thus achieving to the best of our knowledge the best results of univariate methods on this dataset in the literature.

翻译：本文描述了一类季节性与非季节性时间序列模型，可视为加性和乘性指数平滑模型的推广。该类模型的发展受到快速增长、波动性强的时间序列的驱动，并借助最新贝叶斯拟合技术得以实现。在M3竞赛数据集上的应用表明，该模型优于竞赛中的最优算法及其他基准模型，从而据我们所知取得了文献中该数据集上单变量方法的最佳结果。