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…
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…
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…
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…