  Predictive process monitoring aims to predict future characteristics of an
ongoing process case, such as case outcome or remaining timestamp. Recently,
several predictive process monitoring methods based on deep learning such as
Long Short-Term Memory or Convolutional Neural Network have been proposed to
address the problem of next event prediction. However, due to insufficient
training data or sub-optimal network configuration and architecture, these
approaches do not generalize well the problem at hand. This paper proposes a
novel adversarial training framework to address this shortcoming, based on an
adaptation of Generative Adversarial Networks (GANs) to the realm of sequential
temporal data. The training works by putting one neural network against the
other in a two-player game (hence the adversarial nature) which leads to
predictions that are indistinguishable from the ground truth. We formally show
that the worst-case accuracy of the proposed approach is at least equal to the
accuracy achieved in non-adversarial settings. From the experimental evaluation
it emerges that the approach systematically outperforms all baselines both in
terms of accuracy and earliness of the prediction, despite using a simple
network architecture and a naive feature encoding. Moreover, the approach is
more robust, as its accuracy is not affected by fluctuations over the case
length.
