  The increasing volume of space objects in Earth's orbit presents a
significant challenge for Space Situational Awareness (SSA). And in particular,
accurate orbit prediction is crucial to anticipate the position and velocity of
space objects, for collision avoidance and space debris mitigation. When
performing Orbit Prediction (OP), it is necessary to consider the impact of
non-conservative forces, such as atmospheric drag and gravitational
perturbations, that contribute to uncertainty around the future position of
spacecraft and space debris alike. Conventional propagator methods like the
SGP4 inadequately account for these forces, while numerical propagators are
able to model the forces at a high computational cost. To address these
limitations, we propose an orbit prediction algorithm utilizing machine
learning. This algorithm forecasts state vectors on a spacecraft using past
positions and environmental variables like atmospheric density from external
sources. The orbital data used in the paper is gathered from precision
ephemeris data from the International Laser Ranging Service (ILRS), for the
period of almost a year. We show how the use of machine learning and
time-series techniques can produce low positioning errors at a very low
computational cost, thus significantly improving SSA capabilities by providing
faster and reliable orbit determination for an ever increasing number of space
objects.
