We study the spatio-temporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatio-temporal 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 non-stationary spatio-temporal 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.
翻译:我们研究时空预测问题,并采用新的点处理预测算法。由于机器学习文献中存在着犯罪、地震和社会事件预测等关键的实际应用,对时空预测进行了广泛研究。尽管进行了这些透彻的研究,但应用领域固有的具体问题尚未得到充分探讨。在这里,我们在人口稠密和分布不广的序列中处理非静止时空预测问题。我们采用了一种概率办法,将空间域分成各分区域,并用相互作用点处理的模型来模拟每个区域的事件。我们的算法可以通过基于梯度的优化程序,共同学习这些地区之间的空间分隔和相互作用。最后,我们展示了我们模拟数据和两个真实生活数据集的算法的性。我们的方法与基线和最先进的深层学习方法进行了比较,我们在这方面取得了显著的绩效改进。此外,我们还展示了通过实证结果使用不同参数对总体业绩的影响,并解释了选择参数的程序。