Pedestrian trajectory forecasting is an active field of research in which the goal is to predict the path of an agent over a period of time, given some previously observed past motion history. This predicted period of time can be long or short, leading to long-term forecasting or short-term forecasting. In this thesis, we deal with short-term trajectory forecasting which consists in a forecasting window of about five seconds in the future. Having a model capable of these kinds of predictions is useful in many applications such as for service robots, autonomous driving or anomaly detection, where predicting a possible outcome is necessary to plan future moves.