Predictive Business Process Monitoring with LSTM Neural Networks

7 Dec 2016  ·  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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Multivariate Time Series Forecasting BPI challenge '12 LSTM Accuracy 0.7600 # 2
Multivariate Time Series Forecasting Helpdesk LSTM Accuracy 0.7123 # 2


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