Temporal Point Processes (TPP) are probabilistic generative frameworks. They model discrete event sequences localized in continuous time. Generally, real-life events reveal descriptive information, known as marks. Marked TPPs model time and marks of the event together for practical relevance. Conditioned on past events, marked TPPs aim to learn the joint distribution of the time and the mark of the next event. For simplicity, conditionally independent TPP models assume time and marks are independent given event history. They factorize the conditional joint distribution of time and mark into the product of individual conditional distributions. This structural limitation in the design of TPP models hurt the predictive performance on entangled time and mark interactions. In this work, we model the conditional inter-dependence of time and mark to overcome the limitations of conditionally independent models. We construct a multivariate TPP conditioning the time distribution on the current event mark in addition to past events. Besides the conventional intensity-based models for conditional joint distribution, we also draw on flexible intensity-free TPP models from the literature. The proposed TPP models outperform conditionally independent and dependent models in standard prediction tasks. Our experimentation on various datasets with multiple evaluation metrics highlights the merit of the proposed approach.

翻译：时间点过程（TPP）是一种概率生成框架，用于建模连续时间中局部化的离散事件序列。通常，现实事件会附带描述性信息，称为标记。标记TPP将事件的时间和标记共同建模以提高实际相关性。在给定历史事件的条件下，标记TPP旨在学习下一事件时间与标记的联合分布。为简化计算，条件独立TPP模型假设在给定事件历史时时间与标记相互独立，从而将条件联合分布分解为各自条件分布的乘积。这种TPP模型设计中的结构限制损害了在时间与标记交互纠缠场景下的预测性能。本研究通过建模时间与标记的条件相互依赖性来克服条件独立模型的局限。我们构建了一个多元TPP，其时间分布不仅依赖于过去事件，还依赖于当前事件标记。除了传统的基于强度的条件联合分布模型外，我们还借鉴了文献中灵活的免强度TPP模型。所提出的TPP模型在标准预测任务中超越了条件独立及条件依赖模型。我们在多个数据集上采用不同评估指标的实验凸显了该方法的优势。