Normalized L1 Distance

Impact by design: translating Lead times in flux into an R handbook with code ArXiv ID: 2511.12763 “View on arXiv” Authors: Harrison Katz Abstract 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 lead times with a normalized L1 distance and tracks that divergence across months relative to year over year and to a fixed 2018 reference. It also provides a bound that links divergence and remaining horizon to the relative error of pickup forecasts. We implement these ideas end to end in R, using a minimal data schema and providing runnable scripts, simulated examples, and a prespecified evaluation plan. All results use synthetic data so the exposition is fully reproducible without reference to proprietary sources. ...

Lead Times in Flux: Analyzing Airbnb Booking Dynamics During Global Upheavals (2018-2022) ArXiv ID: 2501.10535 “View on arXiv” Authors: Unknown Abstract 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 lead times often overlook important distribution changes. This study introduces a normalized L1 (Manhattan) distance to assess Airbnb booking lead time divergences from 2018 to 2022, focusing on the COVID-19 pandemic across four major U.S. cities. We identify a two-phase disruption: an abrupt change at the pandemic’s onset followed by partial recovery with persistent deviations from pre-2018 patterns. Our method reveals changes in travelers’ planning horizons that standard statistics miss, highlighting the need to analyze the entire lead-time distribution for more accurate demand forecasting and pricing strategies. The normalized L1 metric provides valuable insights for tourism stakeholders navigating ongoing market volatility. ...