AI 中文总结
本文提出一种基于卷积和指数倾斜的事后层次预测方法，用于离散和有界时间序列的相干预测，在模拟和实证中表现优于现有协调方法。
AI 中文摘要
当多元时间序列被迫满足一组聚合约束时，就会出现层次化和分组时间序列，这促使了预测协调方法的发展，以确保此类层次结构的相干预测。许多实际应用涉及离散或有界支撑，这引入了基于高斯协调方法无法解决的额外挑战。我们开发了一种事后层次预测方法，为离散和有界时间序列构建相干预测层次。该方法通过卷积和指数倾斜构建相干预测，在整个层次中保持分布特性和底层支撑。我们将所提出的方法与离散和连续设置中最先进的协调方法进行评估，在一系列实验中展示了强劲的性能。通过流行病学和人口统计数据中的模拟研究和实证应用，我们表明该方法在具有挑战性的场景中提供了可靠且相干的分布预测。
英文摘要
Hierarchical and grouped time series arise when a multivariate time series is forced to satisfy a set of aggregation constraints, motivating forecast reconciliation methods that ensure coherent forecasts of such hierarchical structures. Many real-world applications involve discrete or bounded supports, introducing additional challenges that are not addressed Gaussian-based reconciliation methods. We develop a post-hoc hierarchical forecasting approach to construct coherent forecast hierarchies for discrete and bounded time series. The method constructs coherent forecasts by convolution and exponential tilting, preserving the distributional properties and the underlying support throughout the hierarchy. We evaluate the proposed approach against state-of-the-art reconciliation methods for both discrete and continuous settings, demonstrating strong performance across a range of experiments. Through simulation studies and empirical applications in epidemiological and demographic data, we show that the method provides reliable and coherent distributional forecasts in challenging scenarios.
CommentsThis is a working paper and is not to be circulated. It is for discussion and comment purposes only. This paper has not been peer-reviewed
