# Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction

arXiv: https://arxiv.org/abs/2003.11268 · HTML: https://mlsanity.com/paper/2003.11268/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 30 of 31)

- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences](https://mlsanity.com/paper/2102.07298/) (2021)
- [Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring](https://mlsanity.com/paper/2112.09641/) (2021)
- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)
- [Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning](https://mlsanity.com/paper/2103.11944/) (2021)
- [How do I update my model? On the resilience of Predictive Process Monitoring models to change](https://mlsanity.com/paper/2109.03501/) (2021)
- [Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs](https://mlsanity.com/paper/2104.00362/) (2021)
- [Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks](https://mlsanity.com/paper/2106.08027/) (2021)
- [Modeling Events and Interactions through Temporal Processes -- A Survey](https://mlsanity.com/paper/2303.06067/) (2023)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Tailoring Machine Learning for Process Mining](https://mlsanity.com/paper/2306.10341/) (2023)
- [Generating Privacy-Preserving Process Data with Deep Generative Models](https://mlsanity.com/paper/2203.07949/) (2022)
- [Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning](https://mlsanity.com/paper/2205.03219/) (2022)
- [Predictive Object-Centric Process Monitoring](https://mlsanity.com/paper/2207.10017/) (2022)
- [Can recurrent neural networks learn process model structure?](https://mlsanity.com/paper/2212.06430/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.09527/) (2025)
- [Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction](https://mlsanity.com/paper/2604.00653/) (2026)
- [Masking Neural Networks Using Reachability Graphs to Predict Process Events](https://mlsanity.com/paper/2108.00404/) (2021)
- [Can deep neural networks learn process model structure? An assessment framework and analysis](https://mlsanity.com/paper/2202.11985/) (2022)
- [Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors](https://mlsanity.com/paper/2206.13109/) (2022)
- [Enhancing the Accuracy of Predictors of Activity Sequences of Business Processes](https://mlsanity.com/paper/2312.05560/) (2023)
- [Generating the Traces You Need: A Conditional Generative Model for Process Mining Data](https://mlsanity.com/paper/2411.02131/) (2024)
- [Log Optimization Simplification Method for Predicting Remaining Time](https://mlsanity.com/paper/2503.07683/) (2025)
- [ProcK: Machine Learning for Knowledge-Intensive Processes](https://mlsanity.com/paper/2109.04881/) (2021)
- [Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring](https://mlsanity.com/paper/2211.16106/) (2022)
- [A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches)](https://mlsanity.com/paper/2407.11043/) (2024)
- [Releasing Differentially Private Event Logs Using Generative Models](https://mlsanity.com/paper/2504.06418/) (2025)
- [Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring](https://mlsanity.com/paper/2507.18293/) (2025)

## Abstract (arXiv)

Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, based on an adaptation of Generative Adversarial Networks (GANs) to the realm of sequential temporal data. The training works by putting one neural network against the other in a two-player game (hence the adversarial nature) which leads to predictions that are indistinguishable from the ground truth. We formally show that the worst-case accuracy of the proposed approach is at least equal to the accuracy achieved in non-adversarial settings. From the experimental evaluation it emerges that the approach systematically outperforms all baselines both in terms of accuracy and earliness of the prediction, despite using a simple network architecture and a naive feature encoding. Moreover, the approach is more robust, as its accuracy is not affected by fluctuations over the case length.