  The early outcome prediction of ongoing or completed processes confers
competitive advantage to organizations. The performance of classic machine
learning and, more recently, deep learning techniques such as Long Short-Term
Memory (LSTM) on this type of classification problem has been thorougly
investigated. Recently, much research focused on applying Convolutional Neural
Networks (CNN) to time series problems including classification, however not
yet to outcome prediction. The purpose of this paper is to close this gap and
compare CNNs to LSTMs. Attention is another technique that, in combination with
LSTMs, has found application in time series classification and was included in
our research. Our findings show that all these neural networks achieve
satisfactory to high predictive power provided sufficiently large datasets.
CNNs perfom on par with LSTMs; the Attention mechanism adds no value to the
latter. Since CNNs run one order of magnitude faster than both types of LSTM,
their use is preferable. All models are robust with respect to their
hyperparameters and achieve their maximal predictive power early on in the
cases, usually after only a few events, making them highly suitable for runtime
predictions. We argue that CNNs' speed, early predictive power and robustness
should pave the way for their application in process outcome prediction.
