Time-series forecasts play a critical role in business planning. However,
forecasters typically optimize objectives that are agnostic to downstream
business goals and thus can produce forecasts misaligned with business
preferences. In this work, we demonstrate that optimization of conventional
forecasting metrics can often lead to sub-optimal downstream business
performance. Focusing on the inventory management setting, we derive an
efficient procedure for computing and optimizing proxies of common downstream
business metrics in an end-to-end differentiable manner. We explore a wide
range of plausible cost trade-off scenarios, and empirically demonstrate that
end-to-end optimization often outperforms optimization of standard
business-agnostic forecasting metrics (by up to 45.7% for a simple scaling
model, and up to 54.0% for an LSTM encoder-decoder model). Finally, we discuss
how our findings could benefit other business contexts.

时序预测对于商业规划起重要作用，但常规预测度量的优化往往无视下游业务目标，导致与商业偏好不符的预测结果。本研究关注库存管理领域，以一种可微的端到端方式计算和优化常见的下游业务度量的代理指标，通过大量的成本权衡情景实证表明端到端优化通常优于对标准商业无关预测度量的优化（对于简单缩放模型最高可达 45.7%，对于 LSTM 编码 - 解码模型最高可达 54.0%）。最后，讨论了研究结果对其他商业环境的潜在益处。