LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances

TL;DR

This paper introduces an LSTM-based approach for predicting the remaining time of business process instances by leveraging event data, enabling better process management under service level agreements.

Contribution

It presents a novel deep learning method that utilizes arbitrary event information for accurate remaining time prediction in business processes.

Findings

LSTM model achieves high prediction accuracy on real-world datasets.

Incorporating event-specific data improves forecast precision.

The approach outperforms existing methods in process time prediction.

Abstract

Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.