  Business process simulation is a well-known approach to estimate the impact
of changes to a process with respect to time and cost measures -- a practice
known as what-if process analysis. The usefulness of such estimations hinges on
the accuracy of the underlying simulation model. Data-Driven Simulation (DDS)
methods leverage process mining techniques to learn process simulation models
from event logs. Empirical studies have shown that, while DDS models adequately
capture the observed sequences of activities and their frequencies, they fail
to accurately capture the temporal dynamics of real-life processes. In
contrast, generative Deep Learning (DL) models are better able to capture such
temporal dynamics. The drawback of DL models is that users cannot alter them
for what-if analysis due to their black-box nature. This paper presents a
hybrid approach to learn process simulation models from event logs wherein a
(stochastic) process model is extracted via DDS techniques, and then combined
with a DL model to generate timestamped event sequences. An experimental
evaluation shows that the resulting hybrid simulation models match the temporal
accuracy of pure DL models, while partially retaining the what-if analysis
capability of DDS approaches.
