Predicting Quarterly Hong Kong Tourism Demand Growth Rates, Directional Changes and Turning Points with Composite Leading Indicators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study predicts numerical demand growth rates, directional changes and turning points in the growth rate using the single input leading indicator model and assesses its forecasting performance with the ARIMA model and the no-change model. To assess the forecasting performance from the March quarter of 2004 to the December quarter of 2006, models are fitted to the growth rates of Hong Kong inbound tourism demand from selected tourism markets (Australia, Japan, the UK and the USA). Composite leading indicators for the single input leading indicator model are constructed from selected national leading and lagged indicators. To avoid false signals in turning points, a method is specified to identify the correct turning points in tourism demand growth rates. The prediction performance of these models is then examined, based on the mean absolute percentage error, directional change error and turning point error. A statistical procedure is considered to determine whether the actual and predicted directional changes and turning points are independent or associated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it