the recurrent network cells are ”unrolled”, that is, copies of it are produced for time t, t − 1 , t − 2 , . . . . The state output of the RNN cell for time t − 1 is state input to the cell for time t . In general, t need not represent time, but can index any sequence. Depending on how long one wishes to maintain state for, fewer or more cells are unrolled. Fig. 1 shows an RNN architecture with an input layer, an output layer and two hidden layers that are unrolled five steps (Graves, 2013). Each layer (each box in Fig. 1) in turn consists of multiple input, output, or hidden cells that are not individually shown in Fig. 1. A typical NLP application trains the RNN on sequences of input words to predict the next word, e.g. to provide word suggestions for user input. As shown in Fig 1, the target words are simply the input words shifted by one position, so that for each input word the following word is the target to be predicted. The ability to maintain state or context means that, for example, when given the input ”fox” in Fig. 1 and predicting the correct target (the next word) ”jumps”, the RNN can make use of the previous words ”The quick brown” to improve prediction performance. In this work, we apply a recurrent neural network to the problem of predicting the next event in a process from a sequence of observed events. In implementing process prediction using RNN, our main idea is to view an event log as a text, traces in the log as sentences, and events in a trace as analogous to words in a sentence . Recurrent networks with simple sigmoid cells have unsatisfactory performance for long time or sequence distances, leading to the development of long short term memory (LSTM) cells (Hochreiter and Schmidhuber, 1997), defined by the vector equations in Fig 2. An LSTM cell accepts c$_{t}$$_{−}$$_{1}$ and h$_{t}$$_{−}$$_{1}$ as state information from the prior unrolled cell on the same level, and x$_{t}$ as input from cells on the previous layer. In turn, it passes c$_{t}$ and h$_{t}$ as new state information to the subsequent unrolled 3.3. Long Short Term Memory (LSTM) 7