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

• 成果类型: Article
• 署名作者: Hu, Yu Jeffrey; Rombouts, Jeroen; Wilms, Ines
• 署名单位: Purdue University System; Purdue University; ESSEC Business School; Maastricht University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2023.0130
• 发表日期: 2025
• 关键词: time; selection; package; models; uber

摘要

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.