# LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances

arXiv: https://arxiv.org/abs/1711.03822 · HTML: https://mlsanity.com/paper/1711.03822/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 19 of 19)

- [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)
- [PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/2404.06267/) (2024)
- [Text-Aware Predictive Monitoring of Business Processes](https://mlsanity.com/paper/2104.09962/) (2021)
- [An Explainable Decision Support System for Predictive Process Analytics](https://mlsanity.com/paper/2207.12782/) (2022)
- [Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks](https://mlsanity.com/paper/2106.08027/) (2021)
- [Object-centric Process Predictive Analytics](https://mlsanity.com/paper/2203.02801/) (2022)
- [A Case for Business Process-Specific Foundation Models](https://mlsanity.com/paper/2210.14739/) (2022)
- [Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring](https://mlsanity.com/paper/2506.03696/) (2025)
- [Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes](https://mlsanity.com/paper/2105.05559/) (2021)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring](https://mlsanity.com/paper/2206.06317/) (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)
- [Event Log Sampling for Predictive Monitoring](https://mlsanity.com/paper/2204.01470/) (2022)
- [Universal Multi-Modal Traceformer: Integrating Heterogeneous Context for Process Event Prediction](https://mlsanity.com/paper/2609.24579/) (2026)
- [Extending predictive process monitoring for collaborative processes](https://mlsanity.com/paper/2409.09212/) (2024)
- [Actor-Enriched Time Series Forecasting of Process Performance](https://mlsanity.com/paper/2510.11856/) (2025)
- [Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes](https://mlsanity.com/paper/2608.14367/) (2026)

## Abstract (arXiv)

Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.