obvious clustering of events is discernible for any of our datasets. 

We use LSTMVis (Strobelt et al., 2016), which is motivated by earlier work by Karpathy et al. (2015), to examine the activation of hidden state cells for different inputs $^{6}$. The visualization assists in identifying cells that are active for some input but not others (the ”hypothesis selection process”, Strobelt et al., 2016), and then confirming such hypotheses by comparing activation patterns against similar input (the ”matching process”, Strobelt et al., 2016). 

Figure 10 shows the tool in use with hidden states on the first level of the trained RNN for the BPI2012 dataset with 16 embedding dimensions. The event sequence ”ADECLINED [EOC]” is selected in the timeline (top part of figure). The inputs that match the activation pattern of the selected event sequence are sequences of the same two events, shown at the bottom of the image. This cell appears to represent the ending of cases with declined loan applications. In some cases, another event occurs in between the ”ADECLINED” and ”[EOC]” and this input is represented by the same cell activation pattern. Other hidden state cells represent the events ”WNABELLENOFFERTES”, ”WCOMPLETERENANVRAAG”, and the sequence ”AACCEPTED AFINALIZED OSELECTED”. 

## 7. Discussion and Conclusion 

This paper introduced the use of deep learning for process prediction. Our approach does not rely on explicit process models and can be applied when models do not exist or are difficult to obtain. Our results, surpassing or close to the state- of-the-art and with cross-validated precision in excess of 80% on many problems, demonstrate the feasibility and usefulness of this approach. 

