Evaluating Time Series Models in Short and Long-Term Forecasting of Canadian Air Passenger Data
Bibliographic record
Abstract
This paper uses six time series models to forecast seasonally unadjusted monthly data of Canadian enplaned/deplaned air passengers, for the domestic, transborder and international sectors. We find that forecasting performance of the models varies widely across series and forecast horizons. Our forecasting results are compared with forecasts published by Transport Canada’s Aviation Forecasting Division. / Ce travail utilise six modèles de séries chronologiques afin de prévoir les données mensuelles (sans adjustement saisonniers) des embarquements/atterrissages des passagers aériens canadiens pour les vols domestiques et internationaux. Nous trouvons que la performance dans les prévisions varie grandement selon les séries et selon l’horizon. Nos prévisions sont comparées avec celles publiées par la division des prévisions d’aviation de Transport Canada.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".