Yu Jeffrey Hu, Jeroen Rombouts, Ines Wilms
arXiv 3 Mar 2023 · Econometrics · publishedInformation Systems Research (2024) · 2 citations (OpenAlex)
arXiv:2303.01887 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Killick, R., Fearnhead, P., and Eckley, I. A (2012) Optimal detection of changepoints with a linear computational cost | 0.928 | 5 | 5 | 80% |
| 2 | Luo, L. and Song, P. X.-K (2020) Renewable estimation and incremental inference in generalized linear models with streaming data sets | 0.928 | 4 | 3 | 100% |
| 3 | Taylor, S. J. and Letham, B (2018) Forecasting at scale | 0.737 | 3 | 3 | 67% |
| 4 | Hevner, A., March, S., Park, J., and Ram, S (2004) Design science in information systems research | 0.737 | 3 | 2 | 100% |
| 5 | Pesaran, M. H. and Timmermann, A (2007) Selection of estimation window in the presence of breaks | 0.737 | 3 | 2 | 100% |
| 6 | Garg, N. and Nazerzadeh, H (2022) Driver surge pricing | 0.644 | 2 | 2 | 100% |
| 7 | Guda, H. and Subramanian, U (2019) Your uber is arriving: Managing on-demand workers through surge pricing, forecast communication, and worker incentives | 0.644 | 2 | 2 | 100% |
| 8 | Liu, X., Wang, G. A., Fan, W., and Zhang, Z (2020) Finding useful solutions in online knowledge communities: A theory-driven design and multilevel analysis | 0.644 | 2 | 2 | 100% |
| 9 | Rossi, B (2021) Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them | 0.644 | 2 | 2 | 100% |
| 10 | Wang, Y., Currim, F., and Ram, S (2022) Deep learning of spatiotemporal patterns for urban mobility prediction using big data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 72 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | MLOps Monitoring at Scale for Digital Platforms | 0.644 | 2 | 2 |
| 2 | Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions | 0.405 | 1 | 1 |