Bridging the Gap Between Model Complexity and Data Availability for Forecasting Tourist Activity

Recent years have witnessed a surge in the application of complex machine learning (ML) models across industrial and scientific domains, driven by their ability to outperform traditional statistical methods in specific tasks. However, the efficacy of these models is closely linked to the availability and quality of data, which often limits the complexity and scope of feasible ML approaches. In this paper, we present a novel hybrid forecasting approach that combines ML with classical parametric curve fitting to mitigate data limitations. Specifically, our method first employs an XGBoost-based regression model to predict the residual annual tourist spending from early-season data. These predictions are then incorporated into a generalized logistic growth curve to model the cumulative spending dynamics, yielding interpretable parameters such as total expenditure, seasonal intensity, and peak activity timing. Focusing on the early prediction of annual tourist activity in the Adriatic Croatia region, our empirical evaluation demonstrates that the proposed hybrid model not only outperforms benchmark methods but also delivers actionable insights for real-world tourism planning.