Forecasting International Regional Arrivals in Canada
Bibliographic record
Abstract
ii Considerable research has been done on comparative research models for forecasting tourist arrivals nationally. However, hardly any published study has tested regional international arrival forecasting accuracy. This study focuses upon forecasting arrival to the main regions of entry to Canada, using quarterly international arrival flows into the provinces of Canada from 2000Q1 to 2007Q4. Forecasts are run using the Basic Structural Time Series model (BSM) and the Causal Time Varying Parameter model (TVP) on quarterly data with an ex ante forecasting period 2006Q1 to 2007Q4. Assuming the forecasting process can firstly be shown to operate using time series methods, a further step would be to develop a theoretical model of suitable regional determinant variables for extending the forecasting process into a causal modelling framework. The aim of this study is to determine whether accurate international regional forecasts can be derived; also to assess whether time-series or regression based models derive the most accurate forecasts; and further develop the theory of demand forecasting for regional tourism demand forecasting. Forecasts are made for twelve provinces of Canada regionally and for the whole of Canada nationally in order to test whether accurate international regional forecasts can be derived relative to national arrival forecast. To determine the most accurate forecast, accuracy of the arrival forecasts of each model is measured for each region using the mean absolute percentage error (MAPE) and the root mean square error (RMSE), and compared against the bench mark of a simple naïve model. These forecasts will provide interesting regional forecasts for the first time in Canada and allow for an assessment of the potential use of regional forecasting. iii
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 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".