Jetpack.AI (Polaris)

Jetpack.AI (Polaris) — Airport traffic and capacity forecasting corpus: cleaned global flight schedules (imported from Cirium, SSIM and OAG plus each client's own historical databases), enriched with ML-estimated seat load factors and cargo per flight, and — the rarer asset — the resulting library of airport-authored demand scenarios (typical week through 25-year horizon) with version history showing how each forecast was adjusted.. Small but structurally interesting: a 34-person Belgian data-science firm (~$2M revenue band) with a recent M&A event, sitting on an airport-level forward-supply …

Jetpack.AI (Polaris) offers (Alternative, Reference, Fundamental) — Dense per-airport rather than web-scale: every scheduled movement at a subscribing airport, per day, for historical baselines and forward periods out to 25 years, each with seat capacity, ML load factor and cargo. A mid-size airport at ~100 daily movements yields roughly 36k flight-year rows per historical year, with a far larger forward grid, multiplied again by the duplicated scenario library..

Airport demand and passenger-volume forecasting models; capacity and slot-allocation RL environments; airline route-launch scoring from the airport side; benchmark corpus for time-series forecasting methods (scenario vs actual error); human-in-the-loop correction data for forecast-adjustment models; airport retail/real-estate revenue modelling on forward seat supply; airline financial and noise-abatement models needing forward capacity; macro travel-demand nowcasting.

The data is with 25 years of history.

Coverage spans Europe, US, UK, APAC; Passenger Airlines, Airport Services; alternative, reference, fundamental; equities, fixed_income.