Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
Abstract
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.
History: Olivia Liu Sheng, Senior Editor; Huimin Zhao, Associate Editor.
Funding: I. Wilms was financially supported by the Dutch Research Council (NWO) [Grant VI.Vidi.211.032].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.0130.
