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arXiv · 2303.01887

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

Yu Jeffrey Hu·Jeroen Rombouts·Ines Wilms
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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.

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Yu Jeffrey Hu, Jeroen Rombouts, Ines Wilms. 2023-03-03. Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms. https://arxiv.org/abs/2303.01887

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