# Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring

arXiv: https://arxiv.org/abs/2010.00889 · HTML: https://mlsanity.com/paper/2010.00889/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space](https://mlsanity.com/paper/2402.09063/) (2024)
- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [Byte Pair Encoding for Efficient Time Series Forecasting](https://mlsanity.com/paper/2505.14411/) (2025)
- [Comprehensive Attribute Encoding and Dynamic LSTM HyperModels for Outcome Oriented Predictive Business Process Monitoring](https://mlsanity.com/paper/2506.03696/) (2025)
- [Performance-Preserving Event Log Sampling for Predictive Monitoring](https://mlsanity.com/paper/2301.07624/) (2023)
- [Explainable Artificial Intelligence for Improved Modeling of Processes](https://mlsanity.com/paper/2212.00695/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation](https://mlsanity.com/paper/2406.13283/) (2024)
- [Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.09527/) (2025)
- [Event Log Sampling for Predictive Monitoring](https://mlsanity.com/paper/2204.01470/) (2022)
- [Deltat-Mamba3D: A Time-Aware Spatio-Temporal State-Space Model for Breast Cancer Risk Prediction](https://mlsanity.com/paper/2510.19003/) (2025)
- [Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes](https://mlsanity.com/paper/2608.14367/) (2026)
- [On the Potential of Multi-Task Learning in Predictive Process Monitoring](https://mlsanity.com/paper/2609.13477/) (2026)
- [Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes](https://mlsanity.com/paper/2609.21382/) (2026)

## Abstract (arXiv)

Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next activity and timestamp prediction can help to improve the performance of operational business processes. Recently, many PBPM solutions based on deep learning were proposed by researchers. Due to the sequential nature of event log data, a common choice is to apply recurrent neural networks with long short-term memory (LSTM) cells. We argue, that the elapsed time between events is informative. However, current PBPM techniques mainly use 'vanilla' LSTM cells and hand-crafted time-related control flow features. To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells. T-LSTM cells incorporate the elapsed time between consecutive events inherently to adjust the cell memory. Furthermore, we introduce cost-sensitive learning to account for the common class imbalance in event logs. Our experiments on publicly available benchmark event logs indicate the effectiveness of the introduced techniques.