数字平台中基于机器学习的跨时间预测协调
Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning
AI总结：
针对数字平台高维跨截面与跨时间预测协调的需求，提出一种基于机器学习的非线性分层预测协调方法，能直接自动化生成连贯预测并支持高频决策，在配送与共享单车数据集上验证了有效性。
摘要：
平台业务在数字核心上运行，其决策需要不同截面（如地理区域）和时间聚合（如分钟到天）层级的高维准确预测流。这还需要在层级的所有级别上进行连贯的预测，以确保定价、产品、控制和战略等不同规划单元之间的决策保持一致。鉴于平台数据流具有复杂的特征和相互依赖性，我们引入了一种非线性分层预测协调方法，该方法通过使用流行的机器学习方法，以直接和自动化的方式生成跨时间协调预测。该方法速度足够快，能够支持平台所需的基于预测的高频决策。我们在来自欧洲领先的按需配送平台和纽约市自行车共享系统的独特、大规模流数据集上对我们的框架进行了实证测试。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
