Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings. Probabilistic coherent forecasting is tasked to produce forecasts consistent across levels of aggregation. In this study, we propose to augment neural forecasting architectures with a coherent multivariate mixture output. We optimize the networks with a composite likelihood objective, allowing us to capture time series' relationships while maintaining high computational efficiency. Our approach demonstrates 13.2% average accuracy improvements on most datasets compared to state-of-the-art baselines. We conduct ablation studies of the framework components and provide theoretical foundations for them. To assist related work, the code is available at this https://github.com/Nixtla/neuralforecast.

翻译：大量时间序列数据通常按不同聚合层级组织成层次结构，例如产品分类和地理分组。概率连贯预测的任务是生成在各聚合层级间保持一致的预测。在本研究中，我们提出通过引入连贯多变量混合输出来增强神经预测架构。我们采用复合似然目标函数优化网络，在保持高计算效率的同时捕捉时间序列间的关联关系。与当前最优基线方法相比，我们的方法在多数数据集上实现了平均13.2%的精度提升。我们对该框架的各个组件进行了消融研究，并为其提供了理论基础。为便于相关研究，代码已开源至https://github.com/Nixtla/neuralforecast。