Two-Part Forecasting for Time-Shifted Metrics
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918341613977600 |
|---|---|
| author | Katz, Harrison Savage, Erica Brusch, Kai Thomas |
| author_facet | Katz, Harrison Savage, Erica Brusch, Kai Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11194 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Two-Part Forecasting for Time-Shifted Metrics Katz, Harrison Savage, Erica Brusch, Kai Thomas Applications 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. |
| title | Two-Part Forecasting for Time-Shifted Metrics |
| topic | Applications |
| url | https://arxiv.org/abs/2504.11194 |