  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.
