Katz, Savage, and Brusch propose a two-part forecasting method for sectors where event timing differs from recording time. They treat forecasting as a time-shift operation, using univariate time series for total bookings and a Bayesian Dirichlet Auto-Regressive Moving Average (B-DARMA) model to allocate bookings across trip dates based on lead time. Analysis of Airbnb data shows that this approach is interpretable, flexible, and potentially more accurate for forecasting demand across multiple time axes.

翻译：Katz、Savage和Brusch针对事件发生时间与记录时间存在差异的领域，提出了一种双部分预测方法。该方法将预测视为时间偏移操作，利用单变量时间序列预测总预订量，并采用贝叶斯狄利克雷自回归移动平均（B-DARMA）模型，根据提前期将预订量分配至不同出行日期。对Airbnb数据的分析表明，该方法具有可解释性和灵活性，在跨多时间轴的需求预测中可能具有更高的准确性。