7 citations · 12 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…