Multistream-Based Marked Point Process With Decomposed Cumulative Hazard Functions
Multistream-Based Marked Point Process With Decomposed Cumulative Hazard Functions
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DOI：
10.1162/neco_a_01572
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发表时间：
2023-02
影响因子：
2.9
通讯作者：
Hirotaka Hachiya;Sujun Hong
中科院分区：
文献类型：
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作者：
Hirotaka Hachiya;Sujun Hong
摘要将点过程应用于实际问题时，应根据物理和数学先验知识设计合适的强度函数模型。最近，一种完全可训练的基于深度学习的方法已经被开发用于时间点过程。在这种方法中，能够系统计算自适应强度函数的累积危险函数（CHF）以数据驱动的方式建模。然而，在这种方法中，尽管点过程的许多应用产生各种信息，例如位置、幅度和深度，但是没有考虑事件的标记信息。为了克服这一限制，我们提出了一种完全可训练的标记点处理方法，用于使用多流深度神经网络对分解的CHF进行时间和标记预测建模。我们证明了所提出的方法的有效性，通过实验与合成和真实世界的事件数据。
Abstract When applying a point process to a real-world problem, an appropriate intensity function model should be designed based on physical and mathematical prior knowledge. Recently, a fully trainable deep learning–based approach has been developed for temporal point processes. In this approach, a cumulative hazard function (CHF) capable of systematic computation of adaptive intensity function is modeled in a data-driven manner. However, in this approach, although many applications of point processes generate various kinds of information such as location, magnitude, and depth, the mark information of events is not considered. To overcome this limitation, we propose a fully trainable marked point process method for modeling decomposed CHFs for time and mark prediction using multistream deep neural networks. We demonstrate the effectiveness of the proposed method through experiments with synthetic and real-world event data.