Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Springer, Cham
Abstract
Predicting the remaining cycle time of running cases is one
important use case of predictive process monitoring. Different approaches
that learn from event logs, e.g., relying on an existing representation of
the process or leveraging machine learning approaches, have been pro posed in literature to tackle this problem. Machine learning-based tech niques have shown superiority over other techniques with respect to the
accuracy of the prediction as well as freedom from knowledge about the
underlying process models generating the logs. However, all proposed
approaches learn from complete traces. This might cause delays in start ing new training cycles as usually process instances might last over long
time periods of hours, days, weeks or even months.
In this paper, we propose a machine learning approach that can learn
from incomplete ongoing traces. Using a time-aware survival analysis
technique, we can train a neural network to predict the remaining cycle
time of a running case. Our approach accepts as input both complete and
incomplete traces. We have evaluated our approach on different real-life
datasets and compared it with a state of the art baseline. Results show
that our approach, in many cases, is able to outperform the baseline
approach both in accuracy and training time
Description
Keywords
Predictive process monitoring · Remaining time
prediction · Survival analysis · Incomplete traces
Citation
Baskharon, F., Awad, A., Di Francescomarino, C. (2021). Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-Based Approach. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham.