Pen fouling is an undesired behaviour of fattening pigs, where they excrete in their designated resting area and rest in their designated excretion area. This causes problems with health due to poor hygiene, and requires laborious efforts for the farmer to clean the pen and correct the behaviour. A review of the existing literature suggests that changes in lying behaviour may precede an event of fouling. Furthermore, observing the lying patterns of fattening pigs in the morning before entering the fattening unit, as a means of assessing the risk of imminent pen fouling, is known to be a common strategy among Danish farmers. In this study, we show that machine learning methods, specifically random forests and artificial neural networks, can be made to predict pen fouling in the days leading up to the event, based on the position of the pigs within the pen at specific times of the day. We could not show any added information value from distinguishing between standing/lying behaviour within a given area of the pen, as opposed to simply knowing the pigs’ position. We found that the most information value, relevant for training a method for predicting fouling events, are located in the last 2–3 days before the event occurs and when the pigs are observed during the morning hours before any disturbance. Lastly, we demonstrate a Bayesian ensemble strategy for combining multiple different prediction models, which yield higher performances than the best performing models do on their own.