Data to 7 Oct 2026

Predicting Process Behaviour using Deep Learning

arXiv 1612.04600 ↗First posted 14 Dec 2016cs.LG stat.MLcited by 59

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Abstract (arXiv)

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 13; 2023: 6; 2024: 8; 2025: 10; 2026: 6.

59 papers.

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2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
2021A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequencescited by 17
2021Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoringcited by 16
2024Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Taskscited by 14
2021DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approachcited by 13
2024PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instancescited by 13
2021Process Outcome Prediction: CNN vs. LSTM (with Attention)cited by 12
2021Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoringcited by 12
2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
2023An Assessment of ChatGPT on Log Datacited by 11
2021Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learningcited by 10
2023Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspectivecited by 10

38 more

2024SNAP: Semantic Stories for Next Activity Predictioncited by 8
2021How do I update my model? On the resilience of Predictive Process Monitoring models to changecited by 7
2021What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learningcited by 7
2025Neuro-Symbolic Predictive Process Monitoringcited by 7
2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6
2021Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networkscited by 6
2021Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanismscited by 6
2022Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Modelscited by 5
2024Machine learning in business process management: A systematic literature reviewcited by 5
2025Self-Explaining Neural Networks for Business Process Monitoringcited by 5
2022Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directionscited by 4
2023A Discussion on Generalization in Next-Activity Predictioncited by 4
2023Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Predictioncited by 4
2025Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoringcited by 4
2021Extending LIME for Business Process Automationcited by 3
2022Explainable Predictive Process Monitoring: A User Evaluationcited by 3
2022Predictive Object-Centric Process Monitoringcited by 3
2022A Framework for Extracting and Encoding Features from Object-Centric Event Datacited by 3
2022Can recurrent neural networks learn process model structure?cited by 3
2023Performance-Preserving Event Log Sampling for Predictive Monitoringcited by 3
2022Explainable Artificial Intelligence for Improved Modeling of Processescited by 2
2024Recent Advances in Data-Driven Business Process Managementcited by 2
2025On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Taskscited by 2
2025Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoringcited by 2
2021Process Model Forecasting Using Time Series Analysis of Event Sequence Datacited by 1
2021Masking Neural Networks Using Reachability Graphs to Predict Process Eventscited by 1
2022Can deep neural networks learn process model structure? An assessment framework and analysiscited by 1
2022Event Log Sampling for Predictive Monitoringcited by 1
2022Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighborscited by 1
2023PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networkscited by 1
2025From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizationscited by 1
2026Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruningcited by 1
2021ProcK: Machine Learning for Knowledge-Intensive Processescited by 0
2022A machine learning approach to predict the structural and magnetic properties of Heusler alloy familiescited by 0
2022Encoder-Decoder Model for Suffix Prediction in Predictive Monitoringcited by 0
2024Structural Positional Encoding for knowledge integration in transformer-based medical process monitoringcited by 0
2024Anomaly Correction of Business Processes Using Transformer Autoencodercited by 0
2024Generating the Traces You Need: A Conditional Generative Model for Process Mining Datacited by 0

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