7 citations · 12 across the 13 of their papers we have counts for
13 papers
When Should Forecasting Models Be Re-Specified? A Cost-Sensitive Trigger for Adaptive Model-Form Updating
Harrison Katz
Routine refresh bundles two operations that need not travel together: estimating parameters and selecting the model form. The second is often unnecessary. Under a reduced-update po…
Cost-sensitive retraining via posterior learning debt
Harrison Katz
Deployed prediction systems are often retrained on fixed calendars, even when model staleness and retraining burden vary over time. This short communication formulates retraining f…
Coupled Supply and Demand Forecasting in Platform Accommodation Markets
Harrison Katz
Tourism demand forecasting is methodologically mature, but it typically treats accommodation supply as fixed or exogenous. In platform-mediated short-term rentals, supply is elasti…
Retraining as Approximate Bayesian Inference
Harrison Katz
Model retraining is usually treated as an ongoing maintenance task. But as Harrison Katz now argues, retraining can be better understood as approximate Bayesian inference under com…
Distributional Fitting and Tail Analysis of Lead-Time Compositions: Nights vs. Revenue on Airbnb
Harrison E. Katz, Jess Needleman, Liz Medina
We analyze daily lead-time distributions for two Airbnb demand metrics, Nights Booked (volume) and Gross Booking Value (revenue), treating each day's allocation across 0-365 days a…
Forecasting the Evolving Composition of Inbound Tourism Demand: A Bayesian Compositional Time Series Approach Using Platform Booking Data
Harrison Katz
Understanding how the composition of guest origin markets evolves over time is critical for destination marketing organizations, hospitality businesses, and tourism planners. We de…
Directional-Shift Dirichlet ARMA Models for Compositional Time Series with Structural Break Intervention
Harrison Katz
Compositional time series frequently exhibit structural breaks due to external shocks, policy changes, or market disruptions. Standard methods either ignore such breaks or handle t…
Impact by design: translating Lead times in flux into an R handbook with code
Harrison Katz
This commentary translates the central ideas in Lead times in flux into a practice ready handbook in R. The original article measures change in the full distribution of booking lea…
Centered-Innovation MA for Bayesian Dirichlet ARMA: Theoretical Equivalence and an Application to Bank-Asset Shares
Harrison Katz
We study a minimal change to an observation-driven Bayesian Dirichlet ARMA (B--DARMA) for compositional time series: replace the raw additive log-ratio (ALR) residual in the moving…
Slomads Rising: Stay Length Shifts in Digital Nomad Travel, United States 2019-2024
Harrison Katz, Erica Savage
Using all U.S. Airbnb reservations created in 2019-2024 (booking-count weighted), we quantify pandemic-era shifts in nights per booking (NPB) and the mechanism behind them. The mea…
Forecasting the U.S. Renewable-Energy Mix with an ALR-BDARMA Compositional Time-Series Framework
Harrison Katz, Thomas Maierhofer
Accurate forecasts of the US renewable-generation mix are critical for planning transmission upgrades, sizing storage, and setting balancing-market rules. We present a Bayesian Dir…
Two-Part Forecasting for Time-Shifted Metrics
Harrison Katz, Erica Savage, Kai Thomas Brusch
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 u…
Lead Times in Flux: Analyzing Airbnb Booking Dynamics During Global Upheavals (2018-2022)
Harrison Katz, Erica Savage, Peter Coles
Short-term shifts in booking behaviors can disrupt forecasting in the travel and hospitality industry, especially during global crises. Traditional metrics like average or median l…