On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable, and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe, and find strong performance gains from using our framework against several industry benchmarks, across all geographical regions, loss functions, and both pre- and post-Covid periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs.

翻译：按需服务平台面临着一个具有挑战性的问题：需要对大量表现出不稳定性的高频区域需求数据流进行预测。本文开发了一种新颖的预测框架，该框架快速、可扩展，并能自动评估变化的环境而无需人工干预。我们在欧洲一家领先的按需配送平台的大规模需求数据集上对我们的框架进行了实证测试，发现与多个行业基准相比，在所有地理区域、损失函数以及新冠疫情前后时期，使用我们的框架均能带来显著的性能提升。我们通过计算财务收益和计算成本降低，将这些预测收益转化为该按需服务平台的经济影响。