# Time and Activity Sequence Prediction of Business Process Instances

arXiv: https://arxiv.org/abs/1602.07566 · HTML: https://mlsanity.com/paper/1602.07566/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2016.

## Built on by (most cited first, 24 of 24)

- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [Text-Aware Predictive Monitoring of Business Processes](https://mlsanity.com/paper/2104.09962/) (2021)
- [Object-centric Process Predictive Analytics](https://mlsanity.com/paper/2203.02801/) (2022)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [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)
- [Long-term series forecasting with Query Selector -- efficient model of sparse attention](https://mlsanity.com/paper/2107.08687/) (2021)
- [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)
- [Predictive Object-Centric Process Monitoring](https://mlsanity.com/paper/2207.10017/) (2022)
- [Performance-Preserving Event Log Sampling for Predictive Monitoring](https://mlsanity.com/paper/2301.07624/) (2023)
- [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)
- [Event Log Sampling for Predictive Monitoring](https://mlsanity.com/paper/2204.01470/) (2022)
- [Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors](https://mlsanity.com/paper/2206.13109/) (2022)
- [Nirdizati: an Advanced Predictive Process Monitoring Toolkit](https://mlsanity.com/paper/2210.09688/) (2022)
- [RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes](https://mlsanity.com/paper/2507.02690/) (2025)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [Selecting Optimal Trace Clustering Pipelines with AutoML](https://mlsanity.com/paper/2109.00635/) (2021)
- [Extending predictive process monitoring for collaborative processes](https://mlsanity.com/paper/2409.09212/) (2024)
- [Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment](https://mlsanity.com/paper/2503.07638/) (2025)
- [Promoting Simple Agents: Ensemble Methods for Event-Log Prediction](https://mlsanity.com/paper/2604.21629/) (2026)
- [Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches](https://mlsanity.com/paper/2607.27797/) (2026)

## Abstract (arXiv)

The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but all of them assume that the underling process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which outperforms the state-of-the-art and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on two real case studies.