On the Potential of Multi-Task Learning in Predictive Process Monitoring

arXiv:2609.13477v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution support toward proactive analysis and monitoring. Although deep learning has improved prediction accuracy in PPM, most approaches follow a single-task learning (STL) setup, training a separate model per task. This increases maintenance effort and overlooks potential synergies. Multi-task learning (MTL), which jointly learns multiple prediction targets in one model, offers a promising alternative, yet its effectiveness in PPM remains underexplored. It remains unclear whether and under which settings MTL improves upon STL, which prediction tasks benefit most from joint learning, which task combinations are particularly synergistic, and if and how tasks should be balanced. To fill this gap, we present the first comprehensive empirical study of MTL for PPM, evaluating a variety of task combinations, neural architectures, and optimization methods. Overall, our results position MTL as a strong paradigm for PPM: we see substantial improvements in next-activity prediction and inherent mitigation of class imbalance using MTL, while task balancing is especially critical under low-capacity models.

Related

• GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks
• How Will My Business Process Unfold? Predicting Case Suffixes With Start and End Timestamps
• Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams
• Asymptotic Behavior of Multi--Task Learning: Implicit Regularization and Double Descent Effects

Source: arXiv cs.LG | 2026-09-15