Abstract
NeSy4PPM is the first Python-based library for Predictive Process Monitoring (PPM) that integrates neural models with symbolic background knowledge to improve suffix prediction under specific contextual circumstances. It supports suffix prediction while ensuring compliance with various types of background knowledge, including declare, MP-Declare, ProbDeclare, and procedural models such as Petri nets and BPMN. In this paper, we present the functionalities of NeSy4PPM and empirically evaluate its performance in terms of prediction efficiency, compliance with the input background knowledge, and overall effectiveness.