Intermittent time series, characterised by the presence of a significant amount of zeros, constitute a large percentage of inventory items in supply chain. Probabilistic forecasts are needed to plan the inventory levels; the predictive distribution should cover non-negative values, have a mass in zero and a long upper tail. Intermittent time series are commonly forecast using local models, which are trained individually on each time series. In the last years global models, which are trained on a large collection of time series, have become popular for time series forecasting. Global models are often based on neural networks. However, they have not yet been exhaustively tested on intermittent time series. We carry out the first study comparing state-of-the-art local (iETS, TweedieGP) and global models (D-Linear, DeepAR, Transformers) on intermittent time series. For neural networks models we consider three different distribution heads suitable for intermittent time series: negative binomial, hurdle-shifted negative binomial and Tweedie. We use, for the first time, the last two distribution heads with neural networks. We perform experiments on five large datasets comprising more than 40'000 real-world time series. Among neural networks D-Linear provides best accuracy; it also consistently outperforms the local models. Moreover, it has also low computational requirements. Transformers-based architectures are instead much more computationally demanding and less accurate. Among the distribution heads, the Tweedie provides the best estimates of the highest quantiles, while the negative binomial offers overall the best performance.

翻译：间歇时间序列以存在大量零值为特征，在供应链库存物品中占据很大比例。库存水平规划需要概率预测；预测分布应覆盖非负值，在零点具有质量点，并呈现长上尾。间歇时间序列通常采用局部模型进行预测，这些模型基于各时间序列单独训练。近年来，基于大规模时间序列集合训练的全局模型在时间序列预测领域日益流行。全局模型通常基于神经网络，但尚未在间歇时间序列上得到充分测试。我们开展了首个比较间歇时间序列上先进局部模型（iETS、TweedieGP）与全局模型（D-Linear、DeepAR、Transformers）的研究。针对神经网络模型，我们考虑了三种适用于间歇时间序列的分布输出头：负二项分布、跨栏偏移负二项分布及Tweedie分布。我们首次将后两种分布输出头与神经网络结合使用。在包含超过40,000条真实世界时间序列的五个大型数据集上进行实验。在神经网络中，D-Linear提供了最佳精度，且持续优于局部模型，同时具有较低的计算需求。而基于Transformer的架构计算需求显著更高且精度较低。在分布输出头中，Tweedie对高分位数的估计效果最佳，而负二项分布整体性能最优。