| ID | Forename | Surname | Affiliation |
|---|---|---|---|
| 1 | Fang | Huang | independent |
jubilant-puzzle
China
Description and Rationale
The team south-wind is a solo project run by a data analyst with several years of experience in data analytics and machine learning. After working in larger tech hubs, the participant now lives in Haikou, a coastal city on the south coast of Hainan Island, China, and works locally. The name “south-wind” comes from this setting: doing aviation research from a small coastal city in the south, where the south wind blows year-round.
The team joined the PRC Data Challenge 2026 for three reasons:
To learn and sharpen skills in time-series regression and gradient boosting modelling. Predicting taxi-out time is a concrete, well-scoped problem that is ideal for practising feature engineering, time-series validation and ensemble methods.
To contribute to open aviation research. Even if the final ranking is modest, the team plans to release all code and notes under an open licence so that future participants can build on this work.
To do something useful and a bit different. Aviation operations have real-world impact on emissions and passenger experience, and being able to predict taxi-out time even roughly is genuinely useful. Working on it from the south wind side of the world is the team’s small way to participate in open European aviation research.
The team will work primarily in Python, using XGBoost, LightGBM and CatBoost as the main models, with feature engineering focused on time-of-day, congestion patterns and aircraft-specific factors.
Details
- Type: Independent
- Country: China
- Number of team members: 1