  There is a growing need for empirical benchmarks that support researchers and
practitioners in selecting the best machine learning technique for given
prediction tasks. In this paper, we consider the next event prediction task in
business process predictive monitoring and we extend our previously published
benchmark by studying the impact on the performance of different encoding
windows and of using ensemble schemes. The choice of whether to use ensembles
and which scheme to use often depends on the type of data and classification
task. While there is a general understanding that ensembles perform well in
predictive monitoring of business processes, next event prediction is a task
for which no other benchmarks involving ensembles are available. The proposed
benchmark helps researchers to select a high performing individual classifier
or ensemble scheme given the variability at the case level of the event log
under consideration. Experimental results show that choosing an optimal number
of events for feature encoding is challenging, resulting in the need to
consider each event log individually when selecting an optimal value. Ensemble
schemes improve the performance of low performing classifiers in this task,
such as SVM, whereas high performing classifiers, such as tree-based
classifiers, are not better off when ensemble schemes are considered.
