Cross-temporal forecast reconciliation at digital platforms with machine learning
针对数字平台高维、多层级预测需求，提出一种非线性层级预测协调方法，利用机器学习自动生成跨时间协调的预测，并在欧洲外卖平台和纽约共享单车数据上验证其高效性。
Platform businesses operate on a digital core, and their decision-making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all hierarchy levels to ensure aligned decision-making across different planning units such as pricing, product, controlling, and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces cross-temporal reconciled forecasts in a direct and automated way through popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision-making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle-sharing system in New York City.