Two-Part Forecasting for Time-Shifted Metrics
Applications
2026-02-18 v2
Abstract
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.
Cite
@article{arxiv.2504.11194,
title = {Two-Part Forecasting for Time-Shifted Metrics},
author = {Harrison Katz and Erica Savage and Kai Thomas Brusch},
journal= {arXiv preprint arXiv:2504.11194},
year = {2026}
}