  Since flight delay hurts passengers, airlines, and airports, its prediction
becomes crucial for the decision-making of all stakeholders in the aviation
industry and thus has been attempted by various previous research. However,
previous delay predictions are often categorical and at a highly aggregated
level. To improve that, this study proposes to apply the novel Temporal Fusion
Transformer model and predict numerical airport arrival delays at quarter hour
level for U.S. top 30 airports. Inputs to our model include airport demand and
capacity forecasts, historic airport operation efficiency information, airport
wind and visibility conditions, as well as enroute weather and traffic
conditions. The results show that our model achieves satisfactory performance
measured by small prediction errors on the test set. In addition, the
interpretability analysis of the model outputs identifies the important input
factors for delay prediction.
