# How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps

arXiv: https://arxiv.org/abs/2509.14536 · HTML: https://mlsanity.com/paper/2509.14536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [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)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Agent Miner: An Algorithm for Discovering Agent Systems from Event Data](https://mlsanity.com/paper/2212.01454/) (2022)
- [AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation](https://mlsanity.com/paper/2408.08571/) (2024)
- [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://mlsanity.com/paper/2212.03710/) (2022)
- [Why am I Waiting? Data-Driven Analysis of Waiting Times in Business Processes](https://mlsanity.com/paper/2212.01392/) (2022)
- [Enhancing the Accuracy of Predictors of Activity Sequences of Business Processes](https://mlsanity.com/paper/2312.05560/) (2023)

## Abstract (arXiv)

Predictive process monitoring supports operational decision-making by forecasting future states of ongoing business cases. A key task is case suffix prediction, which estimates the remaining sequence of activities for a case. Most existing approaches only generate activities with a single timestamp (usually the completion time). However, this is insufficient for resource capacity planning, which requires distinguishing between waiting time and processing time to accurately schedule resources and manage workloads. This paper introduces a technique to predict case suffixes that include both start and end timestamps. By predicting distinct waiting and processing intervals, the method provides a more granular view of future resource demands.