  Space debris is a major problem in space exploration. International bodies
continuously monitor a large database of orbiting objects and emit warnings in
the form of conjunction data messages. An important question for satellite
operators is to estimate when fresh information will arrive so that they can
react timely but sparingly with satellite maneuvers. We propose a statistical
learning model of the message arrival process, allowing us to answer two
important questions: (1) Will there be any new message in the next specified
time interval? (2) When exactly and with what uncertainty will the next message
arrive? The average prediction error for question (2) of our Bayesian Poisson
process model is smaller than the baseline in more than 4 hours in a test set
of 50k close encounter events.

Machine Learning;Machine Learning