  Deep-learning models such as Convolutional Neural Networks (CNN) and Long
Short-Term Memory (LSTM) have been successfully used for process-mining tasks.
They have achieved better performance for different predictive tasks than
traditional approaches. We extend the existing body of research by testing four
different variants of Graph Neural Networks (GNN) and a fully connected
Multi-layer Perceptron (MLP) with dropout for the tasks of predicting the
nature and timestamp of the next process activity. In contrast to existing
studies, we evaluate our models' performance at different stages of a process,
determined by quartiles of the number of events and normalized quarters of the
case duration. This provides new insights into the performance of a prediction
model, as they behave differently at different stages of a business-process.
Interestingly, our experiments show that the simple MLP often outperforms more
sophisticated deep-learning models in both prediction tasks. We argue that care
needs to be taken when applying automated process-prediction techniques at
different stages of a process. We further argue that researchers should reflect
their results with strong baselines methods like simple MLPs.
