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<img src='content_image/314328.jpg'>

are parameterized (Table 1). Greff et al. (2015) show that these parameters are sig- nificantly less important in determining RNN performance than network size and are largely independent in their effect on RNN performance. Trainable parameters are initialized using a uniform random distribution over [ − 0 . 1 , 0 . 1]. Training pro- ceeds in batches of size 20. For each batch, the backpropagation algorithm computes the mean gradients for all parameters. Training of the net proceeds in ”epochs”. Each epoch trains the net on the entire event log. Subsequent epochs maintain the weights W and biases b learned from the previous epoch but reinitialize the states for each layer and then train the net again on the entire event log. The net was trained for 100 epochs. The learning rate is reduced from 1 by a factor of 0 . 75 each epoch after the 50th. Dropout is a technique to prevent overfitting and improve learning by randomly temporarily removing a cell from the network (Srivastava et al., 2014). Dropout probability for each cell is 0 . 2. 

## 5. Experimental Method 

## 5.1. Data 

To provide a compelling evaluation of our approach, it should be compared to the state-of-the-art in next event prediction. Of the related work discussed in Section 2, only Breuker et al. (2016) make an implementation publicly available and 

