Papers › Predictive Business Process Monitoring with LSTM Neural Networks
Predictive Business Process Monitoring with LSTM Neural Networks
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multivariate Time Series Forecasting | BPI challenge '12 | LSTM | Accuracy | 0.76 | #2 of 2 | Archive leaderboard | report |
| Multivariate Time Series Forecasting | Helpdesk | LSTM | Accuracy | 0.7123 | #2 of 2 | Archive leaderboard | report |
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