Applying Bidirectional Long Short-Term Memories (BLSTM) to Performance Data in Air Traffic Management for System Identification

The performance analysis of complex systems like Air Traffic Management (ATM) is a challenging task. To overcome statistical complexities through analysing non-linear time series we approach the problem with machine learning methods. Therefore we understand ATM (and its identified system model) as a system of coupled and interdependent sub-systems working in time-continous processes, measurable through time-discrete time series.

In this paper we discuss the requirements of a system identification process and the attached statistical analysis of ATM emitted performance data based on discussed benchmarking frameworks. The superior aim is to show, that neural networks are able to handle complex non-linear time-series, to learn how to rebuild them considering multidimenional inputs and to store knowledge about the observation data set’s behavior.