# Predictive Process Monitoring Methods: Which One Suits Me Best?

arXiv: https://arxiv.org/abs/1804.02422 · HTML: https://mlsanity.com/paper/1804.02422/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 27 of 27)

- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [Prescriptive Process Monitoring: Quo Vadis?](https://mlsanity.com/paper/2112.01769/) (2021)
- [Counterfactual Explanations for Predictive Business Process Monitoring](https://mlsanity.com/paper/2202.12018/) (2022)
- [Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective](https://mlsanity.com/paper/2304.06412/) (2023)
- [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)
- [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)
- [Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches](https://mlsanity.com/paper/2303.09209/) (2023)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report](https://mlsanity.com/paper/2109.00287/) (2021)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction](https://mlsanity.com/paper/2312.08847/) (2023)
- [Explainable Predictive Process Monitoring: A User Evaluation](https://mlsanity.com/paper/2202.07760/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring](https://mlsanity.com/paper/2502.10573/) (2025)
- [Process Model Forecasting Using Time Series Analysis of Event Sequence Data](https://mlsanity.com/paper/2105.01092/) (2021)
- [Learning to act: a Reinforcement Learning approach to recommend the best next activities](https://mlsanity.com/paper/2203.15398/) (2022)
- [Nirdizati: an Advanced Predictive Process Monitoring Toolkit](https://mlsanity.com/paper/2210.09688/) (2022)
- [Log Optimization Simplification Method for Predicting Remaining Time](https://mlsanity.com/paper/2503.07683/) (2025)
- [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)
- [Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles](https://mlsanity.com/paper/2303.14939/) (2023)
- [Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment](https://mlsanity.com/paper/2503.07638/) (2025)
- [Working My Way Back to You: Resource-Centric Next-Activity Prediction](https://mlsanity.com/paper/2508.19016/) (2025)
- [How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps](https://mlsanity.com/paper/2509.14536/) (2025)
- [David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM](https://mlsanity.com/paper/2606.15868/) (2026)

## Abstract (arXiv)

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.