Event data consisting of time of occurrence of the events arises in several real-world applications. Recent works have introduced neural network based point processes for modeling event-times, and were shown to provide state-of-the-art performance in predicting event-times. However, neural point process models lack a good uncertainty quantification capability on predictions. A proper uncertainty quantification over event modeling will help in better decision making for many practical applications. Therefore, we propose a novel point process model, Bayesian Neural Hawkes process (BNHP) which leverages uncertainty modelling capability of Bayesian models and generalization capability of the neural networks to model event occurrence times. We augment the model with spatio-temporal modeling capability where it can consider uncertainty over predicted time and location of the events. Experiments on simulated and real-world datasets show that BNHP significantly improves prediction performance and uncertainty quantification for modelling events.
翻译:由事件发生时间构成的事件数据出现在几个现实世界的应用中。最近的工作为模拟事件的时间引入了神经网络基点进程,并显示在预测事件的时间中提供最先进的性能。然而,神经点进程模型缺乏预测预测方面良好的不确定性量化能力。对事件模型进行适当的不确定性量化将有助于为许多实际应用更好地作出决策。因此,我们提议了一个新型点进程模型,即Bayesian Neural Hawkes进程(BNHP),利用Bayesian模型的不确定性建模能力以及神经网络的一般化能力来模拟事件发生时间。我们用spatio-时间建模能力扩大模型,以便考虑事件预测时间和地点的不确定性。模拟和现实世界数据集实验显示,BNHP大大改进了模型活动的预测性能和不确定性量化。