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.

翻译：在现实场景中，许多现象会产生一系列在连续时间中发生的事件。点过程为这些事件序列的建模提供了一种自然的数学框架。在本综述中，我们研究了通过时间过程对事件序列进行建模的概率模型。我们重新审视了事件建模的概念，并提供了该领域文献所依赖的数学基础。我们定义了一个本体论，将现有方法按三类进行归类：简单点过程、标记点过程和时空点过程。针对每一类，我们系统性地回顾了基于深度学习的现有方法。最后，我们分析了所提出的技术可用于处理预测与建模方面的场景。