Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms

Yu Jeffrey Hu (), Jeroen Rombouts () and Ines Wilms ()
Additional contact information
Yu Jeffrey Hu: Daniels School of Business, Purdue University, West Lafayette, Indiana 47907
Jeroen Rombouts: Essec Business School, 95000 Cergy, France
Ines Wilms: Department of Quantitative Economics, Maastricht University, 6211 LK Maastricht, Netherlands

Information Systems Research, 2025, vol. 36, issue 1, 552-571

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.

Keywords: e-commerce; platform econometrics; streaming data; forecast breakdown (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/isre.2023.0130 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:36:y:2025:i:1:p:552-571

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().