Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917680002367488 |
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| author | Hu, Yu Jeffrey Rombouts, Jeroen Wilms, Ines |
| author_facet | Hu, Yu Jeffrey Rombouts, Jeroen Wilms, Ines |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_01887 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms Hu, Yu Jeffrey Rombouts, Jeroen Wilms, Ines Econometrics Applications 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. |
| title | Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms |
| topic | Econometrics Applications |
| url | https://arxiv.org/abs/2303.01887 |