Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring
This addresses data scarcity issues in business process prediction, offering an incremental improvement through novel augmentation techniques for domain-specific applications.
The paper tackles the problem of limited variability and small size in real-world event logs for Predictive Process Monitoring (PPM) by introducing SiamSA-PPM, a self-supervised learning framework that combines Siamese learning with statistical augmentation, achieving competitive or superior performance compared to state-of-the-art methods in next activity and final outcome prediction tasks.
Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches are often limited by the low variability and small size of real-world event logs. To address this, we introduce SiamSA-PPM, a novel self-supervised learning framework that combines Siamese learning with Statistical Augmentation for Predictive Process Monitoring. It employs three novel statistically grounded transformation methods that leverage control-flow semantics and frequent behavioral patterns to generate realistic, semantically valid new trace variants. These augmented views are used within a Siamese learning setup to learn generalizable representations of process prefixes without the need for labeled supervision. Extensive experiments on real-life event logs demonstrate that SiamSA-PPM achieves competitive or superior performance compared to the SOTA in both next activity and final outcome prediction tasks. Our results further show that statistical augmentation significantly outperforms random transformations and improves variability in the data, highlighting SiamSA-PPM as a promising direction for training data enrichment in process prediction.