Forecasting

Probabilistic Forecasting in Travel Retail - Part 1: Foundations

How to build robust forecasting models that account for uncertainty, seasonality, and external shocks in the travel retail sector.

Cyril Noirot
By Cyril NoirotSeptember 1, 20256 min read
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Travel retail presents unique forecasting challenges: extreme seasonality, dependency on flight schedules, economic shocks, and sudden policy changes. Traditional point forecasts fail to capture the inherent uncertainty in this volatile sector.

Point forecasts provide a single "best guess" but offer no insight into uncertainty. Probabilistic forecasting generates entire probability distributions, enabling:

- Risk-aware decisions: Understanding downside scenarios - Inventory optimization: Balancing stockouts vs. overstock - Scenario planning: Preparing for multiple futures - Confidence intervals: Quantifying forecast reliability

In travel retail, the difference between 50th and 95th percentile forecasts often exceeds 300%, making uncertainty quantification critical for operational planning.

About the author

Cyril Noirot

Cyril Noirot

Lead Data Scientist

Freelance data scientist. I design and ship decision systems — forecasting, pricing, marketing measurement, optimization.

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