Monthly passenger forecast per airline, 12 months ahead
We forecast monthly passengers for each of 10 airlines 12 months ahead from a public dataset. In testing on past data, the selected method (the AI forecasting model) was off by 11.8% of actual volume on average, 38% less error than repeating the same month last year.
The request
"Forecast monthly passenger counts for each airline and terminal for the next 12 months so we can plan gate assignments, staffing and concession leases."
The forecast
We expect about 48.2 million passengers over the next 12 months across all 10 series, about 4,018,191 per month on average.
How accurate was it?
We hid the most recent stretch of history, forecast it using only what came before, and compared with what really happened. We did that 5 times, each time 12 months ahead, for every method below. The typical error is the share of actual volume the forecast missed by, added up across all 10 airlines.
The selected method, an AI forecasting model that was not trained on this data, was off by 11.8% on average, against 19.1% for simply repeating the same month last year.
| Method | Kind | Typical error | Actual inside 80% range |
|---|---|---|---|
| AI forecasting model Selected | AI | 11.8% | 78% |
| Theta method | Classic | 12.6% | 89% |
| Exponential smoothing | Classic | 16.9% | 80% |
| Repeat the last value | Simple rule | 16.3% | 82% |
| Repeat last season | Simple rule | 19.1% | 76% |
What the engine noticed in the data
- 4 months with no rows were filled by interpolation, because passengers is otherwise never close to zero (more likely missing data than a closed business). If you were closed on those months, tell us and we will treat them as zero.
Technical details
- Data
- SFO Air Traffic Passenger Statistics (San Francisco International Airport via DataSF). 10 airlines, monthly, from 1999-07-01 to 2026-07-01.
- How we prepared the data
- Monthly passengers per operating airline (enplaned + deplaned + transit, all regions and terminals summed). Keeps the 10 largest airlines. Terminal-level forecasts from the request are out of scope for this case.
- Testing
- 5 rolling tests, each 12 months ahead. Selection metric: WAPE (weighted absolute percentage error, the "typical error" above). Also reported: MASE 0.99 for the selected method.
- Methods
- Chronos-2 (AI foundation model), Theta, Exponential smoothing (ETS), Naive (last value), Seasonal naive.
- Reproduce
- The case folder, spec and outputs are in the 4castPlannr repository under
cases/sfo-air-traffic-passengers/.
Data source and license
Source: San Francisco International Airport, Air Traffic Passenger Statistics, DataSF (data.sf.gov), ODC PDDL.
License: ODC Public Domain Dedication and License (PDDL) 1.0. Source: original data. The forecasts and charts on this page are derived from that data and carry the same attribution.
Have a decision like this?
This case is an example of staffing plans for call centers and service businesses. Send us your own export and question, and we will run the same tests on your data.
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