Existing hierarchical forecasting techniques scale poorly when the number of
time series increases. We propose to learn a coherent forecast for millions of
time series with a single bottom-level forecast model by using a sparse loss
function that directly optimizes the hierarchical product and/or temporal
structure. The benefit of our sparse hierarchical loss function is that it
provides practitioners a method of producing bottom-level forecasts that are
coherent to any chosen cross-sectional or temporal hierarchy. In addition,
removing the need for a post-processing step as required in traditional
hierarchical forecasting techniques reduces the computational cost of the
prediction phase in the forecasting pipeline. On the public M5 dataset, our
sparse hierarchical loss function performs up to 10% (RMSE) better compared to
the baseline loss function. We implement our sparse hierarchical loss function
within an existing forecasting model at bol, a large European e-commerce
platform, resulting in an improved forecasting performance of 2% at the product
level. Finally, we found an increase in forecasting performance of about 5-10%
when evaluating the forecasting performance across the cross-sectional
hierarchies that we defined. These results demonstrate the usefulness of our
sparse hierarchical loss applied to a production forecasting system at a major
e-commerce platform.

通过使用稀疏损失函数直接优化层次产品和 / 或时间结构，我们提出了一种使用单个底层预测模型学习数百万时间序列的连贯预测的方法。我们的稀疏层次损失函数的好处是为从任意选定的横向或时间层次产生连贯底层预测的实践者提供了一种方法。此外，消除了传统层次预测技术中所需的后处理步骤，从而降低了预测流程中的计算成本。在公开的 M5 数据集上，我们的稀疏层次损失函数的表现比基准损失函数提高了 10%（RMSE）。我们将稀疏层次损失函数应用于欧洲一家大型电子商务平台的现有预测模型中，在产品层面上改善了 2% 的预测性能。最后，我们发现在我们定义的横向层次上评估预测性能时，预测性能提高了约 5-10%。这些结果证明了我们的稀疏层次损失应用于主要电子商务平台的生产预测系统的有用性。

规模化的层次预测

Hierarchical Forecasting at Scale

This paper proposes a low latency neural network architecture for event-based
dense prediction tasks. Conventional architectures encode entire scene contents
at a fixed rate regardless of their temporal characteristics. Instead, the
proposed network encodes contents at a proper temporal scale depending on its
movement speed. We achieve this by constructing temporal hierarchy using
stacked latent memories that operate at different rates. Given low latency
event steams, the multi-level memories gradually extract dynamic to static
scene contents by propagating information from the fast to the slow memory
modules. The architecture not only reduces the redundancy of conventional
architectures but also exploits long-term dependencies. Furthermore, an
attention-based event representation efficiently encodes sparse event streams
into the memory cells. We conduct extensive evaluations on three event-based
dense prediction tasks, where the proposed approach outperforms the existing
methods on accuracy and latency, while demonstrating effective event and image
fusion capabilities. The code is available at this https URL

该论文提出了一种用于事件类密集预测任务的低延迟神经网络架构，该架构通过构建分层的时间记忆来在适当的时间尺度上编码内容，并通过基于注意力机制的事件表示将稀疏事件流编码为记忆单元，有效地提高了准确性和性能。