Modeling and Forecasting Inbound Tourism Demand for Long-Haul Markets of Beijing
Bibliographic record
Abstract
This paper aims to identify the most influencing factors of Beijing's inbound tourism demand using the autoregressive distributed lag model (ADLM) and then generates forecasts of international tourist arrivals from the United States, the United Kingdom, and Canada for the period of 2010Q3–2015Q4. The general-to-specific modeling approach was adopted to achieve final models while the exponential smoothing method was used to produce forecasts for independent variables. Results show that factors such as “word of mouth” effect, income level of the origin source markets, the costs of tourism in Beijing, and the cost of tourism in the competing destinations are crucial determinants of the tourism flows from three long-haul international markets. A group of error measures, such as the mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), mean absolute error (MAE), root mean square error (RMSE), and Theil's U statistic, were used to evaluate the forecasting accuracy. The results suggest that all three models have good forecasting abilities with the MAPEs ranging from 5.73% to 14.89%. Implications are discussed and recommendations as well as future research directions are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".