Predictive Business Process Monitoring with LSTM Neural Networks
This addresses the need for more consistent and accurate predictive models in business process management, reducing trial-and-error for users, though it is incremental as it applies an existing neural network method to this domain.
The paper tackles the problem of predictive business process monitoring by proposing LSTM neural networks as a versatile approach that outperforms existing methods in predicting next events, timestamps, and remaining time for running cases.
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-and-error 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.