PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
<|reference_start|>PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances: 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.<|reference_end|>
@article{elyasi2024pgtnet:,
    title={PGTNet: A Process Graph Transformer Network for Remaining Time
  Prediction of Business Process Instances},
    author={Keyvan Amiri Elyasi, Han van der Aa and Heiner Stuckenschmidt},
    journal={arXiv preprint arXiv:2404.06267},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06267},
    primaryClass={cs.LG cs.AI}
}