Abstract: Accurate demand forecasting is critical for optimizing operations in the tourism and hospitality sectors. This paper proposes a robust multi-algorithmic framework leveraging four advanced models of Artificial Intelligence (LSTM, Random Forest, XGBoost, and Prophet) to predict booking trends and customer demand. In contrast to traditional approaches, this study incorporates external factors such as competitors' pricing strategies, local events, and weather patterns, offering a more holistic view of demand drivers. Using a comprehensive dataset from a leading hotel chain, we systematically compare the performance of these models, providing detailed evaluations. The findings offer actionable insights for hotel managers, demonstrating how predictive analytics can inform revenue management, improve operational efficiency, and enhance marketing initiatives. These results contribute to the evolving field of demand forecasting, offering practical recommendations for data-driven decision-making in the tourism and the hospitality sector.

Siham Rekiek, Hakim Jebari and Kamal Reklaoui, “Prediction of Booking Trends and Customer Demand in the Tourism and Hospitality Sector Using AI-Based Models” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151043

@article{Rekiek2024,
title = {Prediction of Booking Trends and Customer Demand in the Tourism and Hospitality Sector Using AI-Based Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151043},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151043},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Siham Rekiek and Hakim Jebari and Kamal Reklaoui}
}