mlsanityData to 14 Oct 2026

LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances

arXiv 1711.03822 ↗First posted 10 Nov 2017cs.LG stat.MLcited by 19

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Abstract (arXiv)

Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 3; 2024: 2; 2025: 2; 2026: 2.

19 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. 2024PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instancescited by 14
  4. 2021Text-Aware Predictive Monitoring of Business Processescited by 9
  5. 2022An Explainable Decision Support System for Predictive Process Analyticscited by 8
  6. 2021Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networkscited by 6
  7. 2022Object-centric Process Predictive Analyticscited by 6
  8. 2022A Case for Business Process-Specific Foundation Modelscited by 5
  9. 2025Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoringcited by 5
  10. 2021Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processescited by 4
  11. 2023Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoringcited by 4
  12. 2022Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoringcited by 3
7 more
  1. 2023Performance-Preserving Event Log Sampling for Predictive Monitoringcited by 3
  2. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  3. 2022Event Log Sampling for Predictive Monitoringcited by 1
  4. 2026Universal Multi-Modal Traceformer: Integrating Heterogeneous Context for Process Event Predictioncited by 1
  5. 2024Extending predictive process monitoring for collaborative processescited by 0
  6. 2025Actor-Enriched Time Series Forecasting of Process Performancecited by 0
  7. 2026Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processescited by 0