Effect of Seasonality Treatment on the Forecasting Performance of Tourism Demand Models
Bibliographic record
Abstract
This study provides a comprehensive comparison of the performance of the commonly used econometric and time-series models in forecasting seasonal tourism demand. The empirical study is carried out based on the demand for outbound leisure tourism by UK residents to seven destination countries: Australia, Canada, France, Greece, Italy, Spain and the USA. In the modelling exercise, the seasonality of the data is treated using the deterministic seasonal dummies, seasonal unit root test techniques and the unobservable component method. The empirical results suggest that no single forecasting technique is superior to the others in all situations. As far as overall forecast accuracy is concerned, the Johansen maximum likelihood error correction model outperforms the other models. The time-series models also show superior performance in dealing with seasonality. However, the time-varying parameter model performs relatively poorly in forecasting seasonal tourism demand. This empirical evidence suggests that the methods of seasonality treatment affect the forecasting performance of the models and that the pre-test for seasonal unit roots is necessary and can improve forecast accuracy.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".