  The current Air Traffic Management (ATM) system worldwide has reached its
limits in terms of predictability, efficiency and cost effectiveness. Different
initiatives worldwide propose trajectory-oriented transformations that require
high fidelity aircraft trajectory planning and prediction capabilities,
supporting the trajectory life cycle at all stages efficiently. Recently
proposed data-driven trajectory prediction approaches provide promising
results. In this paper we approach the data-driven trajectory prediction
problem as an imitation learning task, where we aim to imitate experts
"shaping" the trajectory. Towards this goal we present a comprehensive
framework comprising the Generative Adversarial Imitation Learning state of the
art method, in a pipeline with trajectory clustering and classification
methods. This approach, compared to other approaches, can provide accurate
predictions for the whole trajectory (i.e. with a prediction horizon until
reaching the destination) both at the pre-tactical (i.e. starting at the
departure airport at a specific time instant) and at the tactical (i.e. from
any state while flying) stages, compared to state of the art approaches.
