Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas
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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Time Series Forecasting | BPI challenge '12 | Accuracy | 0.76 | LSTM |
| Time Series Forecasting | Helpdesk | Accuracy | 0.7123 | LSTM |
| Time Series Analysis | BPI challenge '12 | Accuracy | 0.76 | LSTM |
| Time Series Analysis | Helpdesk | Accuracy | 0.7123 | LSTM |
| Multivariate Time Series Forecasting | BPI challenge '12 | Accuracy | 0.76 | LSTM |
| Multivariate Time Series Forecasting | Helpdesk | Accuracy | 0.7123 | LSTM |