  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 combine automated process discovery and enhancement 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 capture the temporal dynamics of real-life
processes. In contrast, parallel work has shown that generative Deep Learning
(DL) models are able to accurately capture such temporal dynamics. The drawback
of these latter 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 from a log using automated process discovery and enhancement
techniques, and this model is then combined with a DL model to generate
timestamped event sequences (traces). An experimental evaluation shows that the
resulting hybrid simulation models match the temporal accuracy of pure DL
models, while retaining the what-if analysis capability of DDS approaches.
