  Event suffix and remaining time prediction are sequence to sequence learning
tasks. They have wide applications in different areas such as economics,
digital health, business process management and IT infrastructure monitoring.
Timestamped event sequences contain ordered events which carry at least two
attributes: the event's label and its timestamp. Suffix and remaining time
prediction are about obtaining the most likely continuation of event labels and
the remaining time until the sequence finishes, respectively. Recent deep
learning-based works for such predictions are prone to potentially large
prediction errors because of closed-loop training (i.e., the next event is
conditioned on the ground truth of previous events) and open-loop inference
(i.e., the next event is conditioned on previously predicted events). In this
work, we propose an encoder-decoder architecture for open-loop training to
advance the suffix and remaining time prediction of event sequences. To capture
the joint temporal dynamics of events, we harness the power of adversarial
learning techniques to boost prediction performance. We consider four real-life
datasets and three baselines in our experiments. The results show improvements
up to four times compared to the state of the art in suffix and remaining time
prediction of event sequences, specifically in the realm of business process
executions. We also show that the obtained improvements of adversarial training
are superior compared to standard training under the same experimental setup.
