Systems and methods for manifolds learning of airline network data

Abstract

Methods and systems for using manifolds learning of airline network traffic to detect airline network traffic disruptions. In an embodiment, a computer processor transmits a request for abnormal airline network traffic simulation data to a simulation device, receives the abnormal airline network traffic simulation data, and generates, utilizing a manifolds learning process, simulated abnormal airline network traffic manifolds representation data. In some embodiments, the process may next include transmitting a request for current airline network traffic data to a current network traffic computer, receiving the current airline network traffic data, and generating a current airline network traffic dataset. In some implementations, the current airline network traffic dataset is then mapped to the abnormal airline network traffic manifolds representation, a determination made that a threshold number of data points of the current airline network traffic dataset maps to the abnormal airline traffic manifolds representation, and then an alarm is transmitted to an operator device indicating that a disruption of airline network traffic is imminent.