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Predictive Business Process Monitoring with LSTM Neural Networks

  • Niek Tax
  • Ilya Verenich
  • Marcello La Rosa
  • Marlon Dumas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10253)

Abstract

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.

Keywords

Recurrent Neural Network Activity Prediction Prediction Point Levenshtein Distance Transition System State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research is funded by the Australian Research Council (grant DP150103356), the Estonian Research Council (grant IUT20-55) and the RISE_BPM project (H2020 Marie Curie Program, grant 645751).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Niek Tax
    • 1
  • Ilya Verenich
    • 2
    • 3
  • Marcello La Rosa
    • 2
  • Marlon Dumas
    • 3
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Queensland University of TechnologyBrisbaneAustralia
  3. 3.University of TartuTartuEstonia

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