Developing an Empirical and Risk-Based Revenue Forecasting Model for NAV CANADA
Bibliographic record
Abstract
Accurate forecasting of aviation activity is necessary for NAV CANADA, because the act that created the company stipulates that revenues must equal cost commitments related to the provision of air navigation services. Accurate forecasting of revenue is an important early step in determining the cost commitments that can be met in upcoming fiscal years. Traditional econometric forecasting techniques are of a long-term nature and do not consider the seasonality of traffic, nor do they provide the required detail at the level of the individual month. For these and other reasons, empirical forecasting methods were developed as the primary forecasting approach for the company. This paper describes the selection and development of the empirical model, which is based on the Holt–Winters multiplicative method, for the air traffic that overflies Canadian airspace on routes between North America and Europe. A second part of the modeling process is the application of risk analysis not only to replace current scenario-based methods but also to improve and augment them. The methodology will then be applied to the two other markets, Asia and the Far East and Alaska, with eventual expansion to Canadian air traffic, though they are not discussed in this paper.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".