Most existing temporal point process models are characterized by conditional intensity function. These models often require numerical approximation methods for likelihood evaluation, which potentially hurts their performance. By directly modelling the integral of the intensity function, i.e., the cumulative hazard function (CHF), the likelihood can be evaluated accurately, making it a promising approach. However, existing CHF-based methods are not well-defined, i.e., the mathematical constraints of CHF are not completely satisfied, leading to untrustworthy results. For multivariate temporal point process, most existing methods model intensity (or density, etc.) functions for each variate, limiting the scalability. In this paper, we explore using neural networks to model a flexible but well-defined CHF and learning the multivariate temporal point process with low parameter complexity. Experimental results on six datasets show that the proposed model achieves the state-of-the-art performance on data fitting and event prediction tasks while having significantly fewer parameters and memory usage than the strong competitors. The source code and data can be obtained from https://github.com/lbq8942/NPP.

翻译：现有大多数时序点过程模型以条件强度函数为特征，这类模型通常需要数值近似方法进行似然评估，这可能损害其性能。通过对强度函数的积分（即累积风险函数，CHF）直接建模，可以精确评估似然，使其成为一种有前景的方法。然而，现有基于CHF的方法定义不完善，即未完全满足CHF的数学约束条件，导致结果不可靠。对于多变量时序点过程，现有方法大多对每个变量单独建模强度（或密度等）函数，限制了可扩展性。本文探索使用神经网络建模灵活但定义完善的CHF，并以较低参数复杂度学习多变量时序点过程。在六个数据集上的实验结果表明，所提模型在数据拟合和事件预测任务上达到最先进性能，同时参数数量和内存占用显著少于强对比方法。源代码与数据可从 https://github.com/lbq8942/NPP 获取。