IEEE Trans Neural Netw Learn Syst. 2023 Jun;34(6):3097-3110. doi: 10.1109/TNNLS.2021.3111817. Epub 2023 Jun 1.
We study the spatiotemporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatiotemporal prediction is extensively studied in machine learning literature due to its critical real-life applications, such as crime, earthquake, and social event prediction. Despite these thorough studies, specific problems inherent to the application domain are not yet fully explored. Here, we address the nonstationary spatiotemporal prediction problem on both densely and sparsely distributed sequences. We introduce a probabilistic approach that partitions the spatial domain into subregions and models the event arrivals in each region with interacting point processes. Our algorithm can jointly learn the spatial partitioning and the interaction between these regions through a gradient-based optimization procedure. Finally, we demonstrate the performance of our algorithm on both simulated data and two real-life datasets. We compare our approach with baseline and state-of-the-art deep learning-based approaches, where we achieve significant performance improvements. Moreover, we also show the effect of using different parameters on the overall performance through empirical results and explain the procedure for choosing the parameters.
我们研究了时空预测问题，并引入了一种新颖的基于点过程的预测算法。由于其在犯罪、地震和社会事件预测等关键现实生活中的应用，时空预测在机器学习文献中得到了广泛的研究。尽管进行了这些深入的研究，但应用领域中固有的特定问题尚未得到充分探索。在这里，我们解决了密集和稀疏分布序列上的非平稳时空预测问题。我们引入了一种概率方法，将空间域划分为子区域，并使用相互作用的点过程对每个区域的事件到达进行建模。我们的算法可以通过基于梯度的优化过程共同学习空间分区和这些区域之间的相互作用。最后，我们在模拟数据和两个真实数据集上展示了我们算法的性能。我们将我们的方法与基线和最先进的基于深度学习的方法进行了比较，在这些方法中我们取得了显著的性能提升。此外，我们还通过实证结果展示了使用不同参数对整体性能的影响，并解释了选择参数的过程。
