| 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 predic-tions 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 de-cision-support. |
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