## 2 Literature Review  
In recent years, tourism demand forecasting (TDF) has significantly benefited from the integration of machine learning (ML) techniques into traditional forecasting frameworks [15]. Approaches such as Artificial Neural Networks (ANNs), AutoRegressive [16] Integrated Moving Average (ARIMA), and Support Vector Regression (SVR) have been extensively explored, demonstrating strengths in modeling nonlinear interactions inherent in tourism dynamics. However, traditional univariate or linear models such as ARIMA, though widely used in early applications [17], often assume stationarity and fail to capture long-range dependencies and structural breaks. Similarly, support vector models suffer from kernel selection sensitivity [18], and neural networks face overfitting risks with limited data [19]. To address such limitations, a range of hybrid models have been proposed. Notably, mixed frameworks combining ARIMA with neural networks or SVMs have shown superior adaptability in capturing both linear and nonlinear components in time series [20; 21]. [PERSON] and [PERSON] [22] further developed a dynamic combination selection algorithm that enhances robustness across variable regional structures. In parallel, the use of empirical mode decomposition with machine learning, as seen in [PERSON] et al. [23], allowed better handling of seasonal variation and noise. Hybrid forecasting models have gained considerable attention due to their effectiveness in addressing the complex and nonlinear nature of tourist behaviors. [PERSON] [24] showed that combining economic models with ML algorithms improves forecasting accuracy and robustness. [PERSON] reinforced these findings, demonstrating how hybrid methodologies leveraging dynamic multi-dimensional feature synthesis outperform single-model approaches in environments subject to rapid changes. Similarly, [PERSON] [25]emphasized the adaptability of hybrid methods in regions with significant demographic and environmental variability. Besides structural fusion, ensemble learning methods, such as a bagging and forecast combination, have also been explored. [PERSON] et al. [26] proposed blending short- and long-term forecasts for tourism flows, yielding a significantly improved accuracy. [PERSON] et al. [27] highlighted the benefit of bootstrap aggregation (bagging) in reducing forecasting variance and improving generalization in TDF contexts. Recent studies have also explored variable and model selection strategies. [PERSON] [28] introduced a novel framework for selecting optimal model structures under tourism-specific data constraints, offering an alternative to fixed-form models. Moreover, [PERSON] et al. [29] applied a fully identified vector error correction model (VECM), revealing valuable insights into demand elasticity and long-run equilibrium in tourism economies. Complementarily, Google search data and online signals have been integrated to enhance the forecasting sensitivity to real-time trends. [PERSON] and [PERSON] [30], followed by [PERSON] and [PERSON] [31], demonstrated that incorporating Google Trends improves the forecast accuracy over traditional time-series baselines. Recent advances highlight the potential of multi-task learning (MTL) frameworks in tourism forecasting, which simultaneously address multiple related prediction tasks, providing richer insights for strategic decision-making. [PERSON] [32] developed a hybrid recommendation system integrating Bayesian inference with content-based strategies to successfully predict tourist arrivals [33] and destination choices concurrently. Furthermore, [PERSON] illustrated the dynamic adaptability of MTL approaches, significantly enhancing the generalization and error resilience. Deep reinforcement learning (DRL) approaches [34] have also emerged as a promising direction for optimizing tourism management strategies under dynamic conditions. [PERSON] et al.