重新发现自下而上：时间层级中的有效预测
Rediscovering Bottom-Up: Effective Forecasting in Temporal Hierarchies
AI总结：
本研究探讨时间层级中预测协调方法对时间聚合ARIMA模型的影响，首次在理论上证明最优方法为自下而上的聚合，并通过模拟和真实数据验证了该发现。
摘要：
预测协调已成为近期预测文献中的重要课题，主要区别在于截面层级与时间层级。本研究聚焦于时间层级，例如将月度时间序列数据聚合为年度数据。我们探讨了各种预测协调方法对时间聚合 ARIMA 模型的影响，从而在理论和实验上连接了层级预测协调与时间聚合领域。本文首次在理论上检验了时间层级预测协调的效果，证明最优方法与自下而上的聚合方法一致。为评估协调后预测的实际意义和性能，我们进行了一系列模拟研究，证实这些发现可推广至更复杂的模型。该结果有助于解释在许多先前研究中观察到的自下而上方法的优异表现。最后，我们将方法应用于真实数据示例，观察到类似的结果。
英文摘要：
Forecast reconciliation has become a prominent topic in recent forecasting literature, with a primary distinction made between cross-sectional and temporal hierarchies. This work focuses on temporal hierarchies, such as aggregating monthly time series data to annual data. We explore the impact of various forecast reconciliation methods on temporally aggregated ARIMA models, thereby bridging the fields of hierarchical forecast reconciliation and temporal aggregation both theoretically and experimentally. Our paper is the first to theoretically examine the effects of temporal hierarchical forecast reconciliation, demonstrating that the optimal method aligns with a bottom-up aggregation approach. To assess the practical implications and performance of the reconciled forecasts, we conduct a series of simulation studies, confirming that the findings extend to more complex models. This result helps explain the strong performance of the bottom-up approach observed in many prior studies. Finally, we apply our methods to real data examples, where we observe similar results.
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。
