We present the Hierarchical Mixture Networks (HINT), a model family for efficient and accurate coherent forecasting. We specialize the networks on the task via a multivariate mixture optimized with composite likelihood and made coherent via bootstrap reconciliation. Additionally, we robustify the networks to stark time series scale variations, incorporating normalized feature extraction and recomposition of output scales within their architecture. We demonstrate 8% sCRPS improved accuracy across five datasets compared to the existing state-of-the-art. We conduct ablation studies on our model's components and extensively investigate the theoretical properties of the multivariate mixture. HINT's code is available at this https://github.com/Nixtla/neuralforecast.

翻译：我们提出分层混合网络（Hierarchical Mixture Networks，简称HINT），这是一种用于高效且准确进行一致预测的模型家族。我们通过复合似然优化的多元混合机制使网络专门化处理该任务，并借助自助法调和实现预测一致性。此外，为增强网络对时间序列尺度剧烈变化的鲁棒性，我们在其架构中融入归一化特征提取与输出尺度的重组。在五个数据集上，相较于现有最优方法，我们展示了8%的sCRPS精度提升。我们对该模型组件进行了消融研究，并深入探讨了多元混合的理论性质。HINT的代码可在以下链接获取：https://github.com/Nixtla/neuralforecast。