  Process mining, a data-driven approach for analyzing, visualizing, and
improving business processes using event logs, has emerged as a powerful
technique in the field of business process management. Process forecasting is a
sub-field of process mining that studies how to predict future processes and
process models. In this paper, we introduce and motivate the problem of event
log prediction and present our approach to solving the event log prediction
problem, in particular, using the sequence-to-sequence deep learning approach.
We evaluate and analyze the prediction outcomes on a variety of synthetic logs
and seven real-life logs and show that our approach can generate perfect
predictions on synthetic logs and that deep learning techniques have the
potential to be applied in real-world event log prediction tasks. We further
provide practical recommendations for event log predictions grounded in the
outcomes of the conducted experiments.
