Explaining Point Processes by Learning Interpretable Temporal Logic Rules

We propose a modeling framework for event data and aim to answer questions such as when and why the next event would happen. Our proposed model excels in small data regime with the ability to incorporate domain knowledge in terms of logic rules. We model the dynamics of the event starts and ends via intensity function with the structures informed by a set of first-order temporal logic rules. Using the softened representation of temporal relations, and a weighted combination of logic rules, our probabilistic model can deal with uncertainty in events. Furthermore, many wellknown point processes (e.g., Hawkes process, selfcorrecting point process) can be interpreted as special cases of our model given simple temporal logic rules. Our model, therefore, riches the family of point processes. We derive a maximum likelihood estimation procedure for the proposed temporal logic model and show that it can lead to accurate predictions when data are sparse and domain knowledge is critical.