NeSy4PPM documentationΒΆ
NeSy4PPM is the first Python package designed for both single-attribute (e.g., activity) and multi-attribute (e.g., activity and resource) suffix prediction in predictive process monitoring. It implements a Neuro-Symbolic (NeSy) system that integrates neural models with various types of symbolic background knowledge (BK), enabling accurate and compliant predictions even under concept drift.
NeSy4PPM offers the following key features:
Symbolic knowledge integration: supports declarative and procedural BK, including DECLARE, MP-DECLARE (multi-perspective DECLARE), ProbDECLARE (probabilistic DECLARE), and Petri nets.
Flexible learning: provides multiple prefix encoding methods and supports LSTM (Long Short-Term Memory) and Transformer architectures.
Drift-aware prediction: contextualizes neural predictions using BK in real-time, enhancing prediction accuracy and compliance in dynamic environments.