Deep-Learning-Driven Spatiotemporal Modeling of Domestic Tourism Dynamics in Thailand

Numerous metrics, such as visitor numbers, tourism net profit, and hotel occupancy rates, are included in the dataset presented in this study, which covers 77 provinces. A baseline-based concept of shock recovery is introduced to measure impact and recovery paths in different regions. Recurrent neural networks incorporate engineered elements that capture seasonality, trend dynamics, shock strength, volatility, and recovery timing. Importantly, latent spatial heterogeneity and cross-regional dependencies are learned within a single architecture by integrating province-level spatiotemporal embeddings. To jointly forecast tourism demand and net profit, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models are created. Using a time-preserving evaluation technique, model performance is assessed against statistical time-series baselines and XGBoost. In early 2020, the results show a structural break that exceeded the 95% decline, along with significantly unequal recovery patterns. The suggested deep learning models surpass baselines by roughly 22–28% in RMSE and 14–16% in MAPE, exhibiting superior ability in capturing spatial heterogeneity and nonlinear recovery dynamics.