# A systematic literature review on state-of-the-art deep learning methods for process prediction

arXiv: https://arxiv.org/abs/2101.09320 · HTML: https://mlsanity.com/paper/2101.09320/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Outcome-Oriented Predictive Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/1707.06766/) (2017)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/1711.03822/) (2017)
- [An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction](https://mlsanity.com/paper/1811.00062/) (2018)
- [Temporal Stability in Predictive Process Monitoring](https://mlsanity.com/paper/1712.04165/) (2017)
- [Classifying Process Instances Using Recurrent Neural Networks](https://mlsanity.com/paper/1809.05896/) (2018)

## Built on by (most cited first, 30 of 32)

- [Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring](https://mlsanity.com/paper/2107.01905/) (2021)
- [Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2407.02310/) (2024)
- [DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach](https://mlsanity.com/paper/2107.08697/) (2021)
- [Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective](https://mlsanity.com/paper/2304.06412/) (2023)
- [Optimization or Architecture: How to Hack Kalman Filtering](https://mlsanity.com/paper/2310.00675/) (2023)
- [SNAP: Semantic Stories for Next Activity Prediction](https://mlsanity.com/paper/2401.15621/) (2024)
- [What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learning](https://mlsanity.com/paper/2110.10225/) (2021)
- [Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks](https://mlsanity.com/paper/2106.08027/) (2021)
- [Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration](https://mlsanity.com/paper/2212.06564/) (2022)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Self-Explaining Neural Networks for Business Process Monitoring](https://mlsanity.com/paper/2503.18067/) (2025)
- [Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring](https://mlsanity.com/paper/2506.03696/) (2025)
- [Tailoring Machine Learning for Process Mining](https://mlsanity.com/paper/2306.10341/) (2023)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Large Process Models: A Vision for Business Process Management in the Age of Generative AI](https://mlsanity.com/paper/2309.00900/) (2023)
- [A Discussion on Generalization in Next-Activity Prediction](https://mlsanity.com/paper/2309.09618/) (2023)
- [The Fragility of Noise Estimation in Kalman Filter: Optimization Can Handle Model-Misspecification](https://mlsanity.com/paper/2104.02372/) (2021)
- [Generating Privacy-Preserving Process Data with Deep Generative Models](https://mlsanity.com/paper/2203.07949/) (2022)
- [XABPs: Towards eXplainable Autonomous Business Processes](https://mlsanity.com/paper/2507.23269/) (2025)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2504.21074/) (2025)
- [Process Model Forecasting Using Time Series Analysis of Event Sequence Data](https://mlsanity.com/paper/2105.01092/) (2021)
- [Continuous Forecasting via Neural Eigen Decomposition](https://mlsanity.com/paper/2202.00117/) (2022)
- [Towards Automated Process Planning and Mining](https://mlsanity.com/paper/2208.08943/) (2022)
- [From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizations](https://mlsanity.com/paper/2508.08061/) (2025)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [A machine learning approach to predict the structural and magnetic properties of Heusler alloy families](https://mlsanity.com/paper/2208.12705/) (2022)
- [Individualized Dosing Dynamics via Neural Eigen Decomposition](https://mlsanity.com/paper/2306.14020/) (2023)
- [Working My Way Back to You: Resource-Centric Next-Activity Prediction](https://mlsanity.com/paper/2508.19016/) (2025)

## Abstract (arXiv)

Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.