## 4 Experiments
### Baselines  
We validated our proposed SE-TFF model by comparing it against several widely used baseline methods. The following methods represent traditional, hybrid, and deep learning \begin{table}
\begin{tabular}{c l} \hline \hline
**Related Information Name** & **Data** \\ \hline Number of records in the dataset & 17,843,127 \\ Number of countries covered & 64 \\ Number of cities included & 743 \\ Number of transportation modes and infrastructure analyzed & 2796 \\ Number of services covered & 204 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relevant statistics of the OPTD dataset. paradigms commonly employed in tourism forecasting, each with unique strengths and clear limitations:  
* Support Vector Regression (SVR): SVR effectively captures complex nonlinear relationships by identifying optimal hyperplanes that minimize prediction errors [39]. However, SVR generally performs poorly with large-scale, high-dimensional data due to increased computational complexity and difficulty in parameter tuning, limiting its scalability in dynamic tourism forecasting scenarios. * Hybrid recommender systems: These methods combine Bayesian preference elicitation with content-based recommendations, utilizing Weighted Extended Jaccard Similarity (WEJS) to personalize tourism predictions [40]. Despite their strong personalization capability, hybrid recommender systems often exhibit limited adaptability to rapidly changing external conditions, such as economic disruptions or pandemics, reducing their effectiveness in volatile environments. * ARIMA (AutoRegressive Integrated Moving Average) models: These capture historical patterns through autoregressive, differencing, and moving average components. While robust for linear, stationary time-series data, ARIMA models fail to adequately capture complex nonlinear dynamics or sudden external shocks, such as pandemics or abrupt policy changes, thereby restricting their predictive accuracy and applicability in complex tourism contexts. * ARIMAX models: Extending ARIMA by incorporating exogenous variables, ARIMAX models enhance the predictive accuracy by considering external factors [41]. Although more flexible than the standard ARIMA, ARIMAX still struggles to model intricate nonlinear relationships and can become overly sensitive to the quality and availability of external data, potentially limiting their reliability when data inputs are sparse or uncertain. * Artificial Neural Networks (ANNs): ANN models excel at capturing complex nonlinear relationships through multiple neuron layers. They have the advantage of modeling intricate dynamic patterns within tourism data, potentially improving the accuracy compared with simpler statistical models. Nonetheless, ANNs are often criticized for their high sensitivity to data sparsity and noise, challenges with interpretability, and computational demands during training, thereby constraining their practicality and robustness in sparse or rapidly changing data environments. The selection of these baselines was grounded in their methodological diversity and historical prominence within the tourism forecasting literature. ARIMA and ARIMAX represent the classical statistical modeling paradigm with strong temporal dependency assumptions. SVR and ANN offer contrasting machine learning perspectives--SVR emphasizes margin-based generalization, while ANN captures nonlinear dependencies via layered architectures. The inclusion of a hybrid recommender model further reflects real-world systems where personalization and context-awareness are essential. These methods span from interpretable linear models to expressive nonlinear learners and serve as suitable benchmarks to evaluate the relative merits of SE-TFF, particularly regarding multi-source integration, feature recalibration, and resilience under data sparsity or shock conditions.