Abstract:

The Middle East and Europe have experienced significant growth in tourism over the last decade, reaching pre-pandemic levels in 2022. Doha, Dubai, and London saw a substantial expansion in their hospitality sectors. With more expected growth, especially in the Middle East due to investments, accurate forecasting is crucial for hoteliers in these competitive markets. The main goal of this study is to comprehensively analyze historical hotel metrics in Doha, Dubai, and London over the past ten years using STR data. This study uses advanced models such as ARIMA, AUTO ARIMA, GARCH, SARIMA, and LSTM to assess and compare their effectiveness in predicting hotel demand and performance indicators, providing insights for the Middle East and European regions. Advanced time series models, including ARIMA, SARIMA, and LSTM, are utilized to assess and compare their efficacy in forecasting hotel demand and KPIs, providing insights for the Middle East and Europe.

Keywords:

Hospitality KIPs, Time series forecasting Models, Machine Learning in Tourism