Cross-border tourism in North America: A hybrid deep learning framework with macroeconomic indicators
Bibliographic record
Abstract
This study proposes a hybrid predictive framework designed to forecast border tourism flows among the United States, Canada, and Mexico. Combining Fuzzy Markov Chains, Hidden Markov Models, and attention-based deep learning architectures (RNNs, GRUs, and CNNs), the model captures the complex and dynamic interplay between exchange rate volatility and broader macroeconomic conditions over forty years. Results show that tourist behavior is shaped by both current economic indicators and long-term economic memory, with attention mechanisms offering interpretable insights into spending and arrival trends. The SUOS model outperforms traditional forecasting approaches, demonstrating superior accuracy and scalability. Its interpretability also enables stakeholders to understand which economic factors drive tourism demand, supporting practical applications such as seasonal planning, marketing timing, and policy formulation. By bridging macroeconomic modeling with advanced AI, this research offers a robust and adaptive tool for anticipating tourism shifts in an increasingly uncertain global economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".