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

TL;DR

This paper introduces time-aware LSTM cells for predictive business process monitoring, effectively modeling elapsed time between events and improving prediction accuracy over traditional methods.

Contribution

The paper proposes a novel time-aware LSTM (T-LSTM) architecture that inherently incorporates elapsed time between events, enhancing predictive performance in business process monitoring.

Findings

T-LSTM outperforms vanilla LSTM in benchmark tests.

Incorporating elapsed time improves prediction accuracy.

Cost-sensitive learning addresses class imbalance effectively.

Abstract

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.