Année
2024
Auteurs
ROMBOUTS Jeroen, Hu Yu Jeffrey, Wilms Ines
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.
HU, Y.J., ROMBOUTS, J. et WILMS, I. (2024). Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms. Information Systems Research, In press, pp. 1-20.