overfitting through cross-validation is therefore essential. 

Our approach constructs the RNN inputs by concatenating categorical, character string valued event attributes and then encodeing these attributes via an embed- ding space. This is feasible only because of the small number of unique values for each attribute in our datasets. Because of this, the number of unique values of the concatenated input remains small. Hence, the embedding space dimensionality need not be large. While the names of executing activities and lifecycle transitions for most datasets usually have a small set of values, the set of resources or organi- zational groups may be much larger, which limits the feasibility of this approach. Application-specific event-level or case-level attributes can also be included in pre- dictors and predictands simply by concatenating their values. This too is limited to cases where the set of unique values is small. Numerical attributes are difficult to handle in our approach unless they are encoded in a limited number of intervals, which may lead to significant information loss. 

A different approach, first encoding individual attributes and subsequently con- catenating the embedding vectors for input to the RNN, can keep the RNN input size small even when the number of unique values for some attributes is large, and also simplifies including numerical attributes. Exploring this alternative and its predic- tive performance, and determining ways to identify event- and case-level attributes that are useful as predictor variables, are important future work. 

## Acknowledgements 

The authors gratefully acknowledge the support of the Memorial University Center for Health Informatics and Analytics, St. John’s, Canada and the Hasso- Plattner-Institute at the University of Potsdam, Germany, in providing access to computing resources. 

