Daily tourism demand forecasting based on a novel Holiformer algorithm : impact of holiday schedule embedding

Abstract

Forecasting tourism demand is crucial but challenging, especially with irregular and non-periodic holidays due to mismatches between lunar and Gregorian calendars and the transfer system. Current methods simplify holidays as dummy variables, overlooking their complex impacts on travel demand. This study introduces an H-temporal embedding technique to incorporate holiday schedules and timestamps and integrates it into the Transformer-based Holiformer model. Using multidimensional data, including holidays, weather, historical arrivals, and search engines, we forecast demand for three destinations before and during the COVID-19 pandemic. The experimental results demonstrate the high accuracy and stability of the Holiformer model. Furthermore, we conducted an in-depth analysis of the relationships between various influencing factors in the Holiformer model and tourist arrivals, revealing that the holiday effect in China has a more pronounced impact on tourist numbers than the holiday effect in the United States. This finding provides a new perspective for tourism demand forecasting.