# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 5 Conclusions  
This paper presents HiP-Transformer, a predictive business monitoring model containing a three-layer Transformer architecture, and focuses on the tasks of the next execution activity prediction and remaining execution time prediction. The model specifically contains an event-level Transformer, subsequence-level Transformer, and instance-level Transformer, which encodes the representation of business process cases at different levels. To better deal with the concept drift phenomenon existing during the execution of business processes, this paper also proposes a concept drift detection algorithm to achieve a valid segmentation of business process cases. It can capture different execution phases of business processes and their distribution changes and provide a basis for modeling the subsequence-level Transformer. This paper conducts an experimental study using several real event logs, and the results show that the HiP-Transformer model has a better performance. Compared with benchmark business process prediction models, the HiP-Transformer's accuracy improves by 6.32% in the next activity prediction, and the mean absolute error reduces by 21% in the remaining time prediction. In addition, we design a comparison experiment to compare the prediction accuracy under different segmentation strategies, and it is found that the drift detection-based segmentation strategy proposed in this paper can achieve higher accuracy in the end. Further experimental analysis on different prefix length reveals that the activity prediction accuracy tends to increase, and the remaining time prediction error tends to decrease with the increase in prefix length and its corresponding feature information, which verifies that the model can enhance the modeling of long business process cases by properly segmenting event subsequences.  
Future work can apply the HiP-Transformer model proposed in this paper to other predictive business monitoring tasks, such as business process execution result prediction, abnormal activity checking, etc., to better validate the value of the method in this paper. Large institutions, such as banks and credit companies, can apply our method on extracting consumer information and other factors to estimate remaining repayment time. The division of different consumer segments enables a more accurate prediction of their payment capacity, which can help to address potential financial risks in advance.  
Conceptualization, W.N. and G.Z.; methodology, W.N.; software, G.Z.; validation, W.N., G.Z. and T.L.; formal analysis, X.X.; investigation, W.N. and G.Z.; data curation, G.Z. and X.X.; writing--original draft preparation, W.N., G.Z., T.L. and Q.Z.; writing--review and editing, W.N., G.Z., T.L. and Q.Z.; funding acquisition, W.N. All authors have read and agreed to the published version of the manuscript.  
The work is supported by Shandong Provincial Natural Science Foundation (No. ZR202 MF319), National Natural Science Foundation of China (No. U1931207), Taishan Scholars Program of Shandong Province (TS20190936), Talented Young Teachers Training Program of Shandong University of Science and Technology (BJ20211110).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical.  
The authors wish to thank all who assisted in conducting this work.  
The authors declare no conflict of interest.