Forecasting the Foreign Tourist Arrivals to Vietnam Using the Autoregressive Integrated Moving Average Method
Bibliographic record
Abstract
The tourism has been recognized as the most important service sector in the economic development strategy of the Vietnamese government for the decades. The number of foreign visitors to Vietnam has increased rapidly over the years, however, the quality of the forecasting work has not met the requirements of the planning development. The serious over-loading of Vietnamese infrastructure as well as serving network has come by the problems in the forecasting. There is a large gap between the forecasting information and the real growth of the tourism sector in Vietnam. In order to solve this issue, our paper employs the ARIMA method to identify a more suitable tool for forecasting the foreign tourist arrivals to Vietnam. The data is used by the monthly form collected from the January 2009 to June 2018. The regression result determines the ARIMA (2,1,12) is the optimal model and applied to forecast the number of visitors come to Vietnam for three months of the third quarter in 2018. Finally, our study result provides a useful forecasting tool for not only the Vietnamese policymakers but also the businesses in the tourism sector in Vietnam in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".