Enhancing Next Activity Prediction with Adversarial Training of Vision Transformers

Predicting the subsequent activity in the ongoing execution (trace) of a business process is a crucial task in Predictive Process Monitoring (PPM). This capability enables analysts to intervene proactively and prevent undesirable behaviors. This paper presents a PPM approach that integrates adversarial training with Vision Transformers (ViTs) to enhance the accuracy of predicting the next activity in a running process trace. This approach takes into account multi-view information that may be captured in a process trace, treating them as distinct patches of an image. Attention modules are employed to reveal explainable information about the different views of a business process and the trace events that could influence the prediction. Additionally, to mitigate overfitting and improve accuracy, we investigate the impact of adversarial ViT training. Experiments conducted on various benchmark event logs demonstrate the effectiveness of the proposed approach compared to several state-of-the-art PPM techniques. Notably, the explanations obtained through attention modules yield valuable insights.