Abstract
This paper introduces a framework that integrates Conformal Prediction (CP) with Predictive Process Monitoring (PPM) to enhance prediction accuracy and reliability by producing prediction intervals with a guaranteed coverage rate. The approach followed fills a significant gap in current research as it provides an effective technique for assessing prediction uncertainty, which is vital for making well-informed decisions in various business sectors. Comprehensive experimental research conducted on various datasets demonstrates the framework's ability and effectiveness in providing accurate and reliable predictions of the remaining time required for the completion of a process trace. This work highlights the significance of measuring uncertainty in predictions, providing a substantial contribution to the areas of PPM and CP. It also offers a solid and trustworthy approach for integrating uncertainty quantification into process mining predictive models that contributes to significantly enhanced decision-support.
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Skouvas, F., Papadopoulos, H., Andreou, A.S. (2024). Enhancing Predictive Process Monitoring with Conformal Prediction. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-031-63215-0_15
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