PuSH - Publikationsserver des Helmholtz Zentrums München

Roider, J.* ; Zanca, D.* ; Eskofier, B.M.

Efficient training of recurrent neural networks for remaining time prediction in predictive process monitoring.

In: (Business Process Management). Berlin [u.a.]: Springer, 2024. 238-255 (Lect. Notes Comput. Sc. ; 14940 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
An important task in predictive process monitoring is the prediction of the remaining time. The accuracy of methods to solve this task has been steadily improved and verified during the last years. The current state-of-the-art uses Long Short-Term Memory (LSTM) neural networks, represented by the DA-LSTM model. However, training such methods requires substantial amounts of time and memory. This paper addresses specifically these problems. We adjust DA-LSTM and introduce DA-LSTM+ which achieves competitive error levels while reducing the training time significantly. Furthermore, we introduce trace-based sequence encoding as an alternative to prefix encoding, and an approach to use case attributes more efficiently to address time and memory limitations during training. The usage of these is not limited to LSTM’s but they are compatible with any neural network type. We evaluate them together with two alternatives for categorical feature encoding in an extensive benchmark study including eight different model architectures based on DA-LSTM+ and 14 publicly available datasets. The study shows that the training of neural network-based methods can be significantly accelerated with our contributions without affecting model’s performance. Our implementation is memory-efficient and publicly available.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Deep Learning ; Predictive Process Monitoring ; Process Mining ; Remaining Time Prediction
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Business Process Management
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14940 LNCS, Heft: , Seiten: 238-255 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)