stead of a HMM, allowing the future hidden state to be a function of both the previous hidden state and the previous observed event (which itself is a probabilis- tic consequence of the previous hidden state). RegPFA uses an EM algorithm to estimate the model parameters of the PFA. The evaluation uses data from the 2012 and 2013 BPI Challenges (van Dongen, 2012, 2013a,b). Our approach has the same objective as these five related works, but differs in terms of method and process representation. Deep Learning in the form of a recurrent neural network (RNN) is used to predict the next events, using event sequences and associated resource information. Processes are only implicitly rep- resented within the RNN, rather than in an explicit state-transition, PFA or HM model. Because of the non-linear transformations used in neural networks, they are particularly suited to model non-linear relationships. In contrast, many of the existing methods are based on linear assumptions (e.g. regression trees). Thus, neural networks require less restrictive assumptions. Moreover, because models are built implicitly rather than explicitly, their performance does not depend on any ex-ante assumptions about the form of the model. Overall, our method constitutes an innovative new approach to process prediction. A neural network is a special form of an acyclic computational graph (Schmid- huber, 2015). It consists of a layer of input cells, one or more layers of ”hidden” cells, and a layer of output cells. Cells in each layer are connected by weighted con- nections to cells in the previous and following layers, allowing for different network architectures. Each cell’s output is a function of the weighted sum of its inputs. A simple network architecture is a fully connected network of cells using sigmoid 5 3. Deep Learning for Process Prediction 3.1. Introduction