PGTNet: A Process Graph Transformer Network for Remaining Time
  Prediction of Business Process Instances
Keyvan Amiri Elyasi Han van der Aa Heiner Stuckenschmidt
We present PGTNet, an approach that transforms event logs into graph datasets
and leverages graph-oriented data for training Process Graph Transformer
Networks to predict the remaining time of business process instances. PGTNet
consistently outperforms state-of-the-art deep learning approaches across a
diverse range of 20 publicly available real-world event logs. Notably, our
approach is most promising for highly complex processes, where existing deep
learning approaches encounter difficulties stemming from their limited ability
to learn control-flow relationships among process activities and capture
long-range dependencies. PGTNet addresses these challenges, while also being
able to consider multiple process perspectives during the learning process.