The hyper Hawkes process (HHP) represents a new family of marked temporal point process models designed to balance flexibility, performance, and interpretability. Unlike traditional models that often trade expressiveness for clarity, HHP aims to deliver both accurate predictions and understandable parameterizations. This approach allows the model to maintain interpretability while enhancing predictive capabilities, addressing a common challenge in the field. The introduction of HHP is regarded as a significant advancement, as it improves upon existing methods by combining these desirable features. Its design enables researchers and practitioners to better analyze and understand temporal event data with marked characteristics. The model’s development reflects ongoing efforts to create more effective and transparent tools for temporal point process modeling. Overall, HHP contributes meaningfully to the advancement of interpretable machine learning models in temporal data analysis.