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

Yu Jeffrey Hu, Jeroen Rombouts*, Ines Wilms

*Corresponding author for this work

Research output: Working paper / PreprintWorking paper


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.
Original languageEnglish
PublisherCornell University - arXiv
Number of pages40
Publication statusPublished - Mar 2023

Publication series

JEL classifications

  • g12 - "Asset Pricing; Trading volume; Bond Interest Rates"


  • e-commerce
  • platform econometrics
  • streaming data
  • forecast breakdown


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