Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-anderror and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.
The authors of REF present a deep learning approach (using LSTM neural network) for predicting the timestamp of the next event and use it to predict the remaining cycle time by repeatedly predicting the timestamp of the next event.
Predictive Business Process Monitoring with LSTM Neural Networks
Lecture Notes in Computer Science, 10253 (2017) 477-492
Computer Science Mathematics