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 levels of the hierarchy 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 the use of 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.

翻译：平台型企业以数字核心为基础运营，其决策需要跨截面（如地理区域）和时间聚合（如分钟到日）不同层级的高维精准预测流。同时，为确保定价、产品、管控与策略等不同规划单元的决策协调一致，必须实现全层级预测的连贯性。鉴于平台数据流具有复杂的特征与相互依赖性，本文提出一种非线性层次预测协调方法，通过运用主流机器学习技术，以直接自动化方式生成跨时序协调预测。该方法具备足够快的计算速度，能够满足平台基于预测的高频决策需求。我们在欧洲领先的按需配送平台与纽约市共享单车系统的独特大规模流式数据集上对框架进行了实证检验。