论文标题
霍克斯超越：神经多事件预测在时空点过程中
Beyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes
论文作者
论文摘要
预测时空中的离散事件具有许多科学应用，例如预测危险地震和传染病的爆发。依赖历史的时空霍克斯过程通常用于数学上对这些点事件进行建模。但是，以前的方法面临许多挑战，尤其是在试图预测未来的事件时。在这项工作中，我们提出了一种新的神经体系结构，用于同时对时空点过程的多事件预测，利用变压器，并通过正常的流和概率层增强。我们的网络对未来离散事件的复杂依赖历史依赖的时空分布进行了批处理预测，从而在包括南加州地震，Citibike，Covid-19和Hawkes Synthetic Pinheel Pinwheel数据集的各种基准数据集上实现了最先进的性能。更普遍地说，我们说明了如何将网络应用于具有关联标记的任何离散事件数据集，即使不知道潜在的物理。
Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. History-dependent spatio-temporal Hawkes processes are often used to mathematically model these point events. However, previous approaches have faced numerous challenges, particularly when attempting to forecast one or multiple future events. In this work, we propose a new neural architecture for simultaneous multi-event forecasting of spatio-temporal point processes, utilizing transformers, augmented with normalizing flows and probabilistic layers. Our network makes batched predictions of complex history-dependent spatio-temporal distributions of future discrete events, achieving state-of-the-art performance on a variety of benchmark datasets including the South California Earthquakes, Citibike, Covid-19, and Hawkes synthetic pinwheel datasets. More generally, we illustrate how our network can be applied to any dataset of discrete events with associated markers, even when no underlying physics is known.
