A direct data aware LSTM neural network architecture for complete remaining trace and runtime prediction
- Author
- Bjorn Rafn Gunnarsson, Seppe vanden Broucke (UGent) and Jochen De Weerdt
- Organization
- Abstract
- Developing LSTM neural networks that can accurately predict the future trajectory of ongoing cases and their remaining runtime is an active area of research in predictive process monitoring. In this work a novel complete remaining trace prediction (CRTP) LSTM is proposed. This model is trained to directly predict the complete remaining trace and runtime of cases in contrast to single event prediction as is considered in previously published research on this topic. This makes the CRTP-LSTM robust in terms of utilizing all available attributes of previously observed events for prediction, consequently it can be considered natively data aware. In an extensive experimental assessment the authors show that CRTP-LSTMs consistently outperform other considered approaches for both remaining trace and runtime prediction. Furthermore, the authors show that including all available information contained in previously observed events has a positive impact on the performance of the CRTP-LSTM model. This indicates that valuable information can be extracted from attributes of events in order to make more accurate trace and runtime predictions. This opens up interesting avenues for future research including the incorporation of inter-case features into a modeling setup when predicting the remaining trace and runtime of cases.
- Keywords
- Information Systems and Management, Computer Networks and Communications, Computer Science Applications, Hardware and Architecture, long short-term memory networks, remaining trace prediction, remaining time prediction, predictive process monitoring, Process mining, Business, Modeling, Computer architecture, Task analysis, Process monitoring, Predictive models, Runtime
Downloads
-
LSTM PPM TSC rev1 Shared by Jochen.pdf
- full text (Accepted manuscript)
- |
- open access
- |
- |
- 891.67 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GSSBHPQTFVRACCSC6N74G2YS
- MLA
- Gunnarsson, Bjorn Rafn, et al. “A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Prediction.” IEEE TRANSACTIONS ON SERVICES COMPUTING, vol. 16, no. 4, 2023, pp. 2330–42, doi:10.1109/tsc.2023.3245726.
- APA
- Gunnarsson, B. R., vanden Broucke, S., & Weerdt, J. D. (2023). A direct data aware LSTM neural network architecture for complete remaining trace and runtime prediction. IEEE TRANSACTIONS ON SERVICES COMPUTING, 16(4), 2330–2342. https://doi.org/10.1109/tsc.2023.3245726
- Chicago author-date
- Gunnarsson, Bjorn Rafn, Seppe vanden Broucke, and Jochen De Weerdt. 2023. “A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Prediction.” IEEE TRANSACTIONS ON SERVICES COMPUTING 16 (4): 2330–42. https://doi.org/10.1109/tsc.2023.3245726.
- Chicago author-date (all authors)
- Gunnarsson, Bjorn Rafn, Seppe vanden Broucke, and Jochen De Weerdt. 2023. “A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Prediction.” IEEE TRANSACTIONS ON SERVICES COMPUTING 16 (4): 2330–2342. doi:10.1109/tsc.2023.3245726.
- Vancouver
- 1.Gunnarsson BR, vanden Broucke S, Weerdt JD. A direct data aware LSTM neural network architecture for complete remaining trace and runtime prediction. IEEE TRANSACTIONS ON SERVICES COMPUTING. 2023;16(4):2330–42.
- IEEE
- [1]B. R. Gunnarsson, S. vanden Broucke, and J. D. Weerdt, “A direct data aware LSTM neural network architecture for complete remaining trace and runtime prediction,” IEEE TRANSACTIONS ON SERVICES COMPUTING, vol. 16, no. 4, pp. 2330–2342, 2023.
@article{01GSSBHPQTFVRACCSC6N74G2YS,
abstract = {{Developing LSTM neural networks that can accurately predict the future trajectory of ongoing cases and their remaining runtime is an active area of research in predictive process monitoring. In this work a novel complete remaining trace prediction (CRTP) LSTM is proposed. This model is trained to directly predict the complete remaining trace and runtime of cases in contrast to single event prediction as is considered in previously published research on this topic. This makes the CRTP-LSTM robust in terms of utilizing all available attributes of previously observed events for prediction, consequently it can be considered natively data aware. In an extensive experimental assessment the authors show that CRTP-LSTMs consistently outperform other considered approaches for both remaining trace and runtime prediction. Furthermore, the authors show that including all available information contained in previously observed events has a positive impact on the performance of the CRTP-LSTM model. This indicates that valuable information can be extracted from attributes of events in order to make more accurate trace and runtime predictions. This opens up interesting avenues for future research including the incorporation of inter-case features into a modeling setup when predicting the remaining trace and runtime of cases.}},
author = {{Gunnarsson, Bjorn Rafn and vanden Broucke, Seppe and Weerdt, Jochen De}},
issn = {{1939-1374}},
journal = {{IEEE TRANSACTIONS ON SERVICES COMPUTING}},
keywords = {{Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,long short-term memory networks,remaining trace prediction,remaining time prediction,predictive process monitoring,Process mining,Business,Modeling,Computer architecture,Task analysis,Process monitoring,Predictive models,Runtime}},
language = {{eng}},
number = {{4}},
pages = {{2330--2342}},
title = {{A direct data aware LSTM neural network architecture for complete remaining trace and runtime prediction}},
url = {{http://doi.org/10.1109/tsc.2023.3245726}},
volume = {{16}},
year = {{2023}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: