  This paper proposes an encoder-decoder architecture grounded on Generative
Adversarial Networks (GANs), that generates a sequence of activities and their
timestamps in an end-to-end way. GANs work well with differentiable data such
as images. However, a suffix is a sequence of categorical items. To this end,
we use the Gumbel-Softmax distribution to get a differentiable continuous
approximation. The training works by putting one neural network against the
other in a two-player game (hence the "adversarial" nature), which leads to
generating suffixes close to the ground truth. From the experimental evaluation
it emerges that the approach is superior to the baselines in terms of the
accuracy of the predicted suffixes and corresponding remaining times, despite
using a naive feature encoding and only engineering features based on control
flow and events completion time.
