Abstract

This study forecasts inbound tourism demand for the United Kingdom, using monthly data from February 1989 to February 2020. In the empirical analysis, we evaluate and compare the performance of five machine learning models (Decision Trees, Random Forests, XGBoost, and Support Vector Regression with the RBF and Linear kernels) against a more traditional linear SARIMA regression model. Forecasting performance metrics included MSE, RMSE, MAE, R², and MAPE. The SVR RBF kernel model achieves the highest accuracy, with a MAPE of 0.014% on the training set. To enhance model interpretability, feature importance analysis is applied to identify the most influential predictors of tourist arrivals. This research offers significant policy implications, aiding government policymakers and private industry stakeholders to optimize their planning and decisions, deploy better long-term business strategy and tourism-related services, optimise the allocation of public and private resources to support the tourism sector.