Modeling Events and Interactions through Temporal Processes -- A Survey
It provides a comprehensive overview for researchers and practitioners working on event modeling in continuous time, but it is incremental as it synthesizes existing literature without introducing new methods.
This survey investigates probabilistic models for modeling event sequences through temporal processes, categorizing existing approaches into three families and systematically reviewing deep learning-based methods for each.
In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.