Detecting Global Events with Bayesian Changepoint Detection on Flight Data


  • Alec Jacobs
  • Ian Kloo



Open-Source Intelligence, Flight Data, Time Series Analysis, Bayesian Changepoint Detection


As a means of enhancing the intelligence community’s ability to be prepared and prompt in the detection of global events, we present a Bayesian changepoint detection methodology to detect anomalous activity in open-source flight data. We demonstrate a computationally inexpensive methodology to monitor a geographic location for changes in flight activity over time and demonstrate its utility in a case study of the 2022 Russian invasion of Ukraine. Beyond flight data, the methodology described in this paper shows the potential to generate near real-time situational awareness using open-source time series data streams. This paper was previously published and presented in the Donald R. Keith Memorial Capstone Conference at USMA in May of 2023.


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How to Cite

Jacobs , A., & Kloo, I. (2023). Detecting Global Events with Bayesian Changepoint Detection on Flight Data. Industrial and Systems Engineering Review, 11(1-2), 87-94.