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What is Revenue Forecasting in Hospitality 2021 about?
This document contains revenue data from 2012-2020 for the travel, tourism, and hospitality food and drink services industry. Two quantitative forecasting methods are demonstrated: 1) a linear regression model with period (year) as the independent variable, and 2) simple exponential smoothing with an alpha of 0.2. The linear regression model has a lower mean absolute percentage error (MAPE) of 11.5% compared to 14.7% for exponential smoothing, indicating it is the better forecasting approach based on foreca
- Author
- Indira Alfonso
- Language
- EN