Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However, most existing methods only consider temporal dynamics without spatial modeling. We propose Deep Spatiotemporal Point Process (DeepSTPP), a deep dynamics model that integrates spatiotemporal point processes. Our method is flexible, efficient, and can accurately forecast irregularly sampled events over space and time. The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process. The intensity function enjoys closed-form integration for the density. The latent process captures the uncertainty of the event sequence. We use amortized variational inference to infer the latent process with deep networks. Using synthetic datasets, we validate our model can accurately learn the true intensity function. On real-world benchmark datasets, our model demonstrates superior performance over state-of-the-art baselines.
翻译:学习时空事件的动态是一个根本问题。 神经点过程会通过深神经网络增强点进程模型的表达性。 然而, 大多数现有方法只考虑时间动态而不进行空间建模。 我们提议深空间时空进程( EepSTPP) 。 我们提出深空间时点进程( Deep Spatotoental Point process (DepSTP) ) 。 我们的方法灵活、高效,并且可以准确预测空间和时间的不定期抽样事件。 我们方法的关键构建是非参数空间时空强度功能, 受潜伏过程的制约。 强度功能享有密度的封闭式集成功能。 潜在过程捕捉事件序列的不确定性。 我们使用分解变异推论来推断深网络的潜伏进程。 我们使用合成数据集验证我们的模型可以准确了解真实的强度函数。 在现实世界的基准数据集中, 我们的模型显示比最先进的基线的性强。