论文标题
通过自我组织决策树的时空点过程进行预测
Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees
论文作者
论文摘要
我们研究了时空预测问题，由于其关键的现实生活应用，这引起了许多研究人员的注意。特别是，我们介绍了一种新颖的方法解决这个问题。我们的方法基于霍克斯的过程，这是一个非平稳和自我激发的过程。我们扩展了可以表示时间序列数据以表示时空数据的标准点过程模型的公式。我们在时间和空间中将数据建模为非平稳性。此外，我们通过自适应决策树将正在进行的空间区域分为子区域，并用单个但相互相互作用的点过程对每个子区域的源统计数据进行建模。我们还为点过程和决策树参数提供了基于梯度的关节优化算法。因此，我们引入了一个可以共同推断空间区域的源统计和适应性分区的模型。最后，我们提供了现实数据的实验结果，与文献中的标准知名方法相比，由于空间适应和关节优化，该结果可显着改善。
We study the spatio-temporal prediction problem, which has attracted the attention of many researchers due to its critical real-life applications. In particular, we introduce a novel approach to this problem. Our approach is based on the Hawkes process, which is a non-stationary and self-exciting point process. We extend the formulations of a standard point process model that can represent time-series data to represent a spatio-temporal data. We model the data as nonstationary in time and space. Furthermore, we partition the spatial region we are working on into subregions via an adaptive decision tree and model the source statistics in each subregion with individual but mutually interacting point processes. We also provide a gradient based joint optimization algorithm for the point process and decision tree parameters. Thus, we introduce a model that can jointly infer the source statistics and an adaptive partitioning of the spatial region. Finally, we provide experimental results on real-life data, which provides significant improvement due to space adaptation and joint optimization compared to standard well-known methods in the literature.
