  Predictive business process monitoring (PBPM) aims to predict future process
behavior during ongoing process executions based on event log data. Especially,
techniques for the next activity and timestamp prediction can help to improve
the performance of operational business processes. Recently, many PBPM
solutions based on deep learning were proposed by researchers. Due to the
sequential nature of event log data, a common choice is to apply recurrent
neural networks with long short-term memory (LSTM) cells. We argue, that the
elapsed time between events is informative. However, current PBPM techniques
mainly use 'vanilla' LSTM cells and hand-crafted time-related control flow
features. To better model the time dependencies between events, we propose a
new PBPM technique based on time-aware LSTM (T-LSTM) cells. T-LSTM cells
incorporate the elapsed time between consecutive events inherently to adjust
the cell memory. Furthermore, we introduce cost-sensitive learning to account
for the common class imbalance in event logs. Our experiments on publicly
available benchmark event logs indicate the effectiveness of the introduced
techniques.
