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 a unique, large-scale streaming dataset from a leading on-demand delivery platform in Europe.

翻译：平台企业以数字化核心为基础运营，其决策需要不同截面（如地理区域）和时间聚合层级（如分钟到天）的高维准确预测流，同时要求所有层级保持预测一致性，以确保定价、产品、控制和战略等不同规划单元的决策协调。鉴于平台数据流具有复杂特征和相互依赖性，我们提出一种非线性层级预测协调方法，通过利用主流机器学习技术，以直接自动化的方式生成跨时间协调预测。该方法具备足够快的运算速度，能够支撑平台所需的高频预测驱动决策。我们基于欧洲领先的按需配送平台的独特大规模流数据集对该框架进行了实证检验。