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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2025. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Information Systems Research, INFORMS, vol. 36(1), pages 552-571, March.
    • Handle: RePEc:inm:orisre:v:36:y:2025:i:1:p:552-571
    DOI: 10.1287/isre.2023.0130
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