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Predictive Business Process Monitoring with LSTM Neural Networks

arXiv 1612.02130 ↗First posted 7 Dec 2016stat.AP cs.DB cs.LGcited by 104

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Abstract (arXiv)

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.

Key facts. Cited by 104 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2016.

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Citing papers per year: 2021: 21; 2022: 19; 2023: 15; 2024: 12; 2025: 20; 2026: 17.

104 papers.

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  1. 2021ProcessTransformer: Predictive Business Process Monitoring with Transformer Networkcited by 37
  2. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  3. 2021A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequencescited by 17
  4. 2021Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoringcited by 16
  5. 2024PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instancescited by 14
  6. 2021Process Outcome Prediction: CNN vs. LSTM (with Attention)cited by 12
  7. 2021Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoringcited by 12
  8. 2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
  9. 2023An Assessment of ChatGPT on Log Datacited by 12
  10. 2021A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processescited by 11
  11. 2021Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learningcited by 11
  12. 2023ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligencecited by 11
38 more
  1. 2023Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspectivecited by 10
  2. 2021Text-Aware Predictive Monitoring of Business Processescited by 9
  3. 2022An Explainable Decision Support System for Predictive Process Analyticscited by 8
  4. 2022Defining Cases and Variants for Object-Centric Event Datacited by 8
  5. 2024SNAP: Semantic Stories for Next Activity Predictioncited by 8
  6. 2024AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulationcited by 8
  7. 2021How do I update my model? On the resilience of Predictive Process Monitoring models to changecited by 7
  8. 2021What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learningcited by 7
  9. 2025Neuro-Symbolic Predictive Process Monitoringcited by 7
  10. 2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6
  11. 2021Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networkscited by 6
  12. 2021Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanismscited by 6
  13. 2022Object-centric Process Predictive Analyticscited by 6
  14. 2023Trace Encoding in Process Mining: a survey and benchmarkingcited by 6
  15. 2025Domain Adaptation of LLMs for Process Datacited by 6
  16. 2022Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Modelscited by 5
  17. 2022A Case for Business Process-Specific Foundation Modelscited by 5
  18. 2024Machine learning in business process management: A systematic literature reviewcited by 5
  19. 2024Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)cited by 5
  20. 2024Achieving Group Fairness through Independence in Predictive Process Monitoringcited by 5
  21. 2025Self-Explaining Neural Networks for Business Process Monitoringcited by 5
  22. 2025Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoringcited by 5
  23. 2021Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processescited by 4
  24. 2021Long-term series forecasting with Query Selector -- efficient model of sparse attentioncited by 4
  25. 2023CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analyticscited by 4
  26. 2023Tailoring Machine Learning for Process Miningcited by 4
  27. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
  28. 2023Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approachcited by 4
  29. 2023Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Predictioncited by 4
  30. 2021Extending LIME for Business Process Automationcited by 3
  31. 2022Explainable Predictive Process Monitoring: A User Evaluationcited by 3
  32. 2022Goal-Oriented Next Best Activity Recommendation using Reinforcement Learningcited by 3
  33. 2022Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoringcited by 3
  34. 2022Predictive Object-Centric Process Monitoringcited by 3
  35. 2022A Framework for Extracting and Encoding Features from Object-Centric Event Datacited by 3
  36. 2022Can recurrent neural networks learn process model structure?cited by 3
  37. 2023Performance-Preserving Event Log Sampling for Predictive Monitoringcited by 3
  38. 2024Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoringcited by 3

50 most cited of 104 All 104 (JSON)